diff --git a/doc/index.rst b/doc/index.rst
index 0bb9b4d24f5..3c486dd2781 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -34,6 +34,7 @@
     tutorials/regions.rst
     tutorials/plot.rst
     tutorials/lines.rst
+    tutorials/vectors.rst
     tutorials/text.rst
     tutorials/contour_map.rst
     tutorials/earth_relief.rst
diff --git a/examples/tutorials/vectors.py b/examples/tutorials/vectors.py
new file mode 100644
index 00000000000..589407bb83e
--- /dev/null
+++ b/examples/tutorials/vectors.py
@@ -0,0 +1,452 @@
+"""
+Plotting vectors
+================
+
+Plotting vectors is handled by :meth:`pygmt.Figure.plot`.
+
+.. note::
+
+    This tutorial assumes the use of a Python notebook, such as IPython or Jupyter Notebook.
+    To see the figures while using a Python script instead, use
+    ``fig.show(method="external")`` to display the figure in the default PDF viewer.
+
+    To save the figure, use ``fig.savefig("figname.pdf")`` where ``"figname.pdf"``
+    is the desired name and file extension for the saved figure.
+"""
+# sphinx_gallery_thumbnail_number = 6
+
+import numpy as np
+import pygmt
+
+########################################################################################
+# Plot Cartesian Vectors
+# ----------------------
+#
+# Create a simple Cartesian vector using a starting point through
+# ``x``, ``y``, and ``direction`` parameters.
+# On the shown figure, the plot is projected on a 10cm X 10cm region,
+# which is specified by the ``projection`` parameter.
+# The direction is specified
+# by a list of two 1d arrays structured as ``[[angle_in_degrees], [length]]``.
+# The angle is measured in degrees and moves counter-clockwise from the
+# horizontal.
+# The length of the vector uses centimeters by default but
+# could be changed using :meth:`pygmt.config`
+# (Check the next examples for unit changes).
+#
+# Notice that the ``v`` in the ``style`` parameter stands for
+# vector; it distinguishes it from regular lines and allows for
+# different customization. ``0c`` is used to specify the size
+# of the arrow head which explains why there is no arrow on either
+# side of the vector.
+
+fig = pygmt.Figure()
+fig.plot(
+    region=[0, 10, 0, 10],
+    projection="X10c/10c",
+    frame="ag",
+    x=2,
+    y=8,
+    style="v0c",
+    direction=[[-45], [6]],
+)
+fig.show()
+
+########################################################################################
+# In this example, we apply the same concept shown previously to plot multiple
+# vectors. Notice that instead of passing int/float to ``x`` and ``y``, a list
+# of all x and y coordinates will be passed. Similarly, the length of direction
+# list will increase accordingly.
+#
+# Additionally, we change the style of the vector to include a red
+# arrow head at the end (**+e**) of the vector and increase the
+# thickness (``pen="2p"``) of the vector stem. A list of different
+# styling attributes can be found in
+# :doc:`Vector heads and tails </gallery/lines/vector_heads_tails>`.
+
+fig = pygmt.Figure()
+fig.plot(
+    region=[0, 10, 0, 10],
+    projection="X10c/10c",
+    frame="ag",
+    x=[2, 4],
+    y=[8, 1],
+    style="v0.6c+e",
+    direction=[[-45, 23], [6, 3]],
+    pen="2p",
+    color="red3",
+)
+fig.show()
+
+########################################################################################
+# The default unit of vector length is centimeters,
+# however, this can be changed to inches or points. Note that, in PyGMT,
+# one point is defined as 1/72 inch.
+#
+# In this example, the graphed region is 5in X 5in, but
+# the length of the first vector is still graphed in centimeters.
+# Using ``pygmt.config(PROJ_LENGTH_UNIT="i")``, the default unit
+# can be changed to inches in the second plotted vector.
+
+fig = pygmt.Figure()
+# Vector 1 with default unit as cm
+fig.plot(
+    region=[0, 10, 0, 10],
+    projection="X5i/5i",
+    frame="ag",
+    x=2,
+    y=8,
+    style="v1c+e",
+    direction=[[0], [3]],
+    pen="2p",
+    color="red3",
+)
+# Vector 2 after changing default unit to inch
+with pygmt.config(PROJ_LENGTH_UNIT="i"):
+    fig.plot(
+        x=2,
+        y=7,
+        direction=[[0], [3]],
+        style="v1c+e",
+        pen="2p",
+        color="red3",
+    )
+fig.show()
+
+########################################################################################
+# Vectors can also be plotted by including all the information
+# about a vector in a single list. However, this requires creating
+# a 2D list or numpy array containing all vectors.
+# Each vector list contains the information structured as:
+# ``[x_start, y_start, direction_degrees, length]``.
+#
+# If this approach is chosen, the ``data`` parameter must be
+# used instead of ``x``, ``y`` and  ``direction``.
+
+# Create a list of lists that include each vector information
+vectors = [[2, 3, 45, 4]]
+
+fig = pygmt.Figure()
+fig.plot(
+    region=[0, 10, 0, 10],
+    projection="X10c/10c",
+    frame="ag",
+    data=vectors,
+    style="v0.6c+e",
+    pen="2p",
+    color="red3",
+)
+fig.show()
+
+########################################################################################
+# Using the functionality mentioned in the previous example,
+# multiple vectors can be plotted at the same time. Another
+# vector could be simply added to the 2D list or numpy
+# array object and passed using ``data`` parameter.
+
+# Vector specifications structured as: [x_start, y_start, direction_degrees, length]
+vector_1 = [2, 3, 45, 4]
+vector_2 = [7.5, 8.3, -120.5, 7.2]
+# Create a list of lists that include each vector information
+vectors = [vector_1, vector_2]
+# Vectors structure: [[2, 3, 45, 4], [7.5, 8.3, -120.5, 7.2]]
+
+fig = pygmt.Figure()
+fig.plot(
+    region=[0, 10, 0, 10],
+    projection="X10c/10c",
+    frame="ag",
+    data=vectors,
+    style="v0.6c+e",
+    pen="2p",
+    color="red3",
+)
+fig.show()
+
+########################################################################################
+# In this example, cartesian vectors are plotted over a Mercator
+# projection of the continental US. The x values represent the
+# longitude and y values represent the latitude where the vector starts.
+#
+# This example also shows some of the styles a vector supports.
+# The beginning point of the vector (**+b**)
+# should take the shape of a circle (**c**). Similarly, the end
+# point of the vector (**+e**) should have an arrow shape (**a**)
+# (to draw a plain arrow, use **A** instead). Lastly, the **+a**
+# specifies the angle of the vector head apex (30 degrees in
+# this example).
+
+# Create a plot with coast, Mercator projection (M) over the continental US
+fig = pygmt.Figure()
+fig.coast(
+    region=[-127, -64, 24, 53],
+    projection="M10c",
+    frame="ag",
+    borders=1,
+    shorelines="0.25p,black",
+    area_thresh=4000,
+    land="grey",
+    water="lightblue",
+)
+
+# Plot a vector using the x, y, direction parameters
+style = "v0.4c+bc+ea+a30"
+fig.plot(
+    x=-110,
+    y=40,
+    style=style,
+    direction=[[-25], [3]],
+    pen="1p",
+    color="red3",
+)
+
+# vector specifications structured as: [x_start, y_start, direction_degrees, length]
+vector_2 = [-82, 40.5, 138, 2.5]
+vector_3 = [-71.2, 45, -115.7, 4]
+# Create a list of lists that include each vector information
+vectors = [vector_2, vector_3]
+
+# Plot vectors using the data parameter.
+fig.plot(
+    data=vectors,
+    style=style,
+    pen="1p",
+    color="yellow",
+)
+fig.show()
+
+########################################################################################
+# Another example of plotting cartesian vectors over a coast plot. This time
+# a Transverse Mercator projection is used. Additionally, :func:`numpy.linspace`
+# is used to create 5 vectors with equal stops.
+
+x = np.linspace(36, 42, 5)  # x values = [36.  37.5 39.  40.5 42. ]
+y = np.linspace(39, 39, 5)  # y values = [39. 39. 39. 39.]
+direction = np.linspace(-90, -90, 5)  # direction values = [-90. -90. -90. -90.]
+length = np.linspace(1.5, 1.5, 5)  # length values = [1.5 1.5 1.5 1.5]
+
+# Create a plot with coast, Mercator projection (M) over the continental US
+fig = pygmt.Figure()
+fig.coast(
+    region=[20, 50, 30, 45],
+    projection="T35/10c",
+    frame=True,
+    borders=1,
+    shorelines="0.25p,black",
+    area_thresh=4000,
+    land="lightbrown",
+    water="lightblue",
+)
+
+fig.plot(
+    x=x,
+    y=y,
+    style="v0.4c+ea+bc",
+    direction=[direction, length],
+    pen="0.6p",
+    color="red3",
+)
+
+fig.show()
+
+########################################################################################
+# Plot Circular Vectors
+# ----------
+#
+# When plotting circular vectors, all of the information for a single vector is
+# to be stored in a list. Each circular vector list is structured as:
+# ``[x_start, y_start, radius, degree_start, degree_stop]``. The first two values in
+# the vector list represent the origin of the circle that will be plotted.
+# The next value is the radius which is represented on the plot in centimeters.
+#
+# The last two values in the vector list represent the degree at which the plot
+# will start and stop. These values are measured counter-clockwise from the horizontal
+# axis. In this example, the result show is the left half of a circle as the
+# plot starts at 90 degrees and goes until 270. Notice that the ``m`` in the
+# ``style`` parameter stands for circular vectors.
+
+fig = pygmt.Figure()
+
+circular_vector_1 = [0, 0, 2, 90, 270]
+data = [circular_vector_1]
+fig.plot(
+    region=[-5, 5, -5, 5],
+    projection="X10c",
+    frame="ag",
+    data=data,
+    style="m0.5c+ea",
+    pen="2p",
+    color="red3",
+)
+
+# Another example using np.array()
+circular_vector_2 = [0, 0, 4, -90, 90]
+data = np.array([circular_vector_2])
+
+fig.plot(
+    data=data,
+    style="m0.5c+ea",
+    pen="2p",
+    color="red3",
+)
+fig.show()
+
+########################################################################################
+# When plotting multiple circular vectors, a two dimensional array or numpy array
+# object should be passed as the ``data`` parameter. In this example, :func:`numpy.column_stack`
+# is used to generate this two dimensional array. Other numpy objects are used to
+# generate linear values for the ``radius`` parameter and random values for
+# the ``degree_stop`` parameter discussed in the previous example. This is
+# the reason in which each vector has
+# a different appearance on the projection.
+
+vector_num = 5
+radius = 3 - (0.5 * np.arange(0, vector_num))
+startdir = np.full(vector_num, 90)
+stopdir = 180 + (50 * np.arange(0, vector_num))
+data = np.column_stack(
+    [np.full(vector_num, 0), np.full(vector_num, 0), radius, startdir, stopdir]
+)
+
+fig = pygmt.Figure()
+fig.plot(
+    region=[-5, 5, -5, 5],
+    projection="X10c",
+    frame="ag",
+    data=data,
+    style="m0.5c+ea",
+    pen="2p",
+    color="red3",
+)
+fig.show()
+
+########################################################################################
+# Much like when plotting Cartesian vectors, the default unit used is centimeters.
+# When this is changed to inches, the size of the plot appears larger when the
+# projection units do not change. Below is an example of two circular vectors.
+# One is plotted using the default unit, and the second is plotted using inches.
+# Despite using the same list to plot the vectors, a different measurement unit
+# causes one to be larger than the other.
+
+circular_vector = [6, 5, 1, 90, 270]
+
+fig = pygmt.Figure()
+fig.plot(
+    region=[0, 10, 0, 10],
+    projection="X10c",
+    frame="ag",
+    data=[circular_vector],
+    style="m0.5c+ea",
+    pen="2p",
+    color="red3",
+)
+
+with pygmt.config(PROJ_LENGTH_UNIT="i"):
+    fig.plot(
+        data=[circular_vector],
+        style="m0.5c+ea",
+        pen="2p",
+        color="red3",
+    )
+fig.show()
+
+########################################################################################
+# Plot Geographic Vectors
+# -----------------------
+# On this map,
+# ``point_1`` and ``point_2`` are coordinate pairs used to set the
+# start and end points of the geographic vector.
+# The geographical vector is going from Idaho to
+# Chicago. To style geographic
+# vectors, use ``=`` at the begining of the ``style`` parameter.
+# Other styling features such as vector stem thickness and head color
+# can be passed into the ``pen`` and ``color`` parameters.
+#
+# Note that the **+s** is added to use a startpoint and an endpoint
+# to represent the vector instead of input angle and length.
+
+point_1 = [-114.7420, 44.0682]
+point_2 = [-87.6298, 41.8781]
+data = np.array([point_1 + point_2])
+
+fig = pygmt.Figure()
+fig.coast(
+    region=[-127, -64, 24, 53],
+    projection="M10c",
+    frame=True,
+    borders=1,
+    shorelines="0.25p,black",
+    area_thresh=4000,
+)
+fig.plot(
+    data=data,
+    style="=0.5c+ea+s",
+    pen="2p",
+    color="red3",
+)
+fig.show()
+
+########################################################################################
+# Using the same technique shown in the previous example,
+# multiple vectors can be plotted in a chain where the endpoint
+# of one is the starting point of another. This can be done
+# by adding the coordinate lists together to create this structure:
+# ``[[start_latitude, start_longitude, end_latitude, end_longitude]]``.
+# Each list within the 2D list contains the start and end information
+# for each vector.
+
+# Coordinate pairs for all the locations used
+ME = [-69.4455, 45.2538]
+CHI = [-87.6298, 41.8781]
+SEA = [-122.3321, 47.6062]
+NO = [-90.0715, 29.9511]
+KC = [-94.5786, 39.0997]
+CA = [-119.4179, 36.7783]
+
+# Add array to piece together the vectors
+data = [ME + CHI, CHI + SEA, SEA + KC, KC + NO, NO + CA]
+
+fig = pygmt.Figure()
+fig.coast(
+    region=[-127, -64, 24, 53],
+    projection="M10c",
+    frame=True,
+    borders=1,
+    shorelines="0.25p,black",
+    area_thresh=4000,
+)
+fig.plot(
+    data=data,
+    style="=0.5c+ea+s",
+    pen="2p",
+    color="red3",
+)
+fig.show()
+
+################################################################################
+# This example plots vectors over a Mercator projection. The starting points are
+# located at SA which is South Africa and going to four different
+# locations.
+
+SA = [22.9375, -30.5595]
+EUR = [15.2551, 54.5260]
+ME = [-69.4455, 45.2538]
+AS = [100.6197, 34.0479]
+NM = [-105.8701, 34.5199]
+data = np.array([SA + EUR, SA + ME, SA + AS, SA + NM])
+
+fig = pygmt.Figure()
+fig.coast(
+    region=[-180, 180, -80, 80],
+    projection="M0/0/12c",
+    frame="afg",
+    land="lightbrown",
+    water="lightblue",
+)
+fig.plot(
+    data=data,
+    style="=0.5c+ea+s",
+    pen="2p",
+    color="red3",
+)
+fig.show()