diff --git a/xarray/core/indexing.py b/xarray/core/indexing.py
index 49360c03b94..75eb0607936 100644
--- a/xarray/core/indexing.py
+++ b/xarray/core/indexing.py
@@ -72,6 +72,41 @@ def _expand_slice(slice_, size):
     return np.arange(*slice_.indices(size))
 
 
+def maybe_convert_to_slice(indexer, size):
+    """Convert an indexer into an equivalent slice object, if possible.
+
+    Arguments
+    ---------
+    indexer : int, slice or np.ndarray
+        If a numpy array, must have integer dtype.
+    size : integer
+        Integer size of the dimension to be indexed.
+    """
+    if indexer.ndim != 1 or not isinstance(indexer, np.ndarray):
+        return indexer
+
+    if indexer.size == 0:
+        return slice(0, 0)
+
+    if indexer.min() < -size or indexer.max() >= size:
+        raise IndexError(
+            'indexer has elements out of bounds for axis of size {}: {}'
+            .format(size, indexer))
+
+    indexer = np.where(indexer < 0, indexer + size, indexer)
+    if indexer.size == 1:
+        i = int(indexer[0])
+        return slice(i, i + 1)
+
+    start = int(indexer[0])
+    step = int(indexer[1] - start)
+    stop = start + step * indexer.size
+    guess = slice(start, stop, step)
+    if np.array_equal(_expand_slice(guess, size), indexer):
+        return guess
+    return indexer
+
+
 def orthogonal_indexer(key, shape):
     """Given a key for orthogonal array indexing, returns an equivalent key
     suitable for indexing a numpy.ndarray with fancy indexing.
@@ -473,24 +508,23 @@ def __init__(self, array):
         self.array = array
 
     def __getitem__(self, key):
-        """ key: tuple of Variable, slice, integer """
-        # basic or orthogonal indexing
-        if all(isinstance(k, (integer_types, slice)) or k.squeeze().ndim <= 1
-               for k in key):
-            value = self.array
-            for axis, subkey in reversed(list(enumerate(key))):
-                if hasattr(subkey, 'squeeze'):
-                    subkey = subkey.squeeze()
-                    if subkey.ndim == 0:  # make at least 1-d array
-                        subkey = subkey.flatten()
-                value = value[(slice(None),) * axis + (subkey,)]
-            return value
+        """ key: tuple of ndarray, slice, integer """
+        if all(isinstance(k, integer_types + (slice,)) for k in key):
+            # basic indexing
+            return self.array[key]
+        elif all(k.shape == (1,) * (i - 1) + (max(k.shape),) + (1,) * (i - 1)
+                 for i, k in enumerate(key)
+                 if isinstance(k, np.ndarray)):
+            # orthogonal indexing
+            # dask only supports one list in an indexer, so convert to slice if
+            # possible
+            key = tuple(maybe_convert_to_slice(np.ravel(k), size)
+                        for k, size in zip(key, self.shape))
+            return self.array[key]
+        # TODO: handle point-wise indexing with vindex
         else:
-            # TODO Dask does not support nd-array indexing.
-            # flatten() -> .vindex[] -> reshape() should be used
-            # instead of `.load()`
-            value = np.asarray(self.array)[key]
-            return value
+            raise IndexError(
+                'dask does not support fancy indexing with key: {}'.format(key))
 
 
 class PandasIndexAdapter(utils.NDArrayMixin):
diff --git a/xarray/tests/test_indexing.py b/xarray/tests/test_indexing.py
index 3866e0511a5..6d478a68b5f 100644
--- a/xarray/tests/test_indexing.py
+++ b/xarray/tests/test_indexing.py
@@ -29,6 +29,30 @@ def test_expanded_indexer(self):
         with self.assertRaisesRegexp(IndexError, 'too many indices'):
             indexing.expanded_indexer(I[1, 2, 3], 2)
 
+    def test_maybe_convert_to_slice(self):
+
+        cases = [
+            (1,),
+            (1, 1),
+            (1, 2),
+            (10,),
+            (0, 10),
+            (5, 10),
+            (5, 8),
+            (None, 5),
+            (None, -3),
+            (0, 10, 2),
+            (10, None, -1),
+            (7, 3, -2),
+        ]
+        for case in cases:
+            slice_obj = slice(*case)
+            base_array = np.arange(*slice_obj.indices(10))
+            for array in [base_array, base_array - 10]:
+                actual = indexing.maybe_convert_to_slice(array, 10)
+                self.assertArrayEqual(np.arange(10)[actual],
+                                      np.arange(10)[slice_obj])
+
     def test_orthogonal_indexer(self):
         x = np.random.randn(10, 11, 12, 13, 14)
         y = np.arange(5)