diff --git a/CHANGELOG.md b/CHANGELOG.md
index fcf702137b3..500410552de 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -18,6 +18,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added pre-gather reduction in the case of `dist_reduce_fx="cat"` to reduce communication cost ([#217](https://github.com/PyTorchLightning/metrics/pull/217))
 
 
+- Added better error message for `AUROC` when `num_classes` is not provided for multiclass input ([#244](https://github.com/PyTorchLightning/metrics/pull/244))
+
+
 - Added support for unnormalized scores (e.g. logits) in `Accuracy`, `Precision`, `Recall`, `FBeta`, `F1`, `StatScore`, `Hamming`, `ConfusionMatrix` metrics ([#200](https://github.com/PyTorchLightning/metrics/pull/200))
 
 
diff --git a/tests/classification/test_auroc.py b/tests/classification/test_auroc.py
index d3196e1ef9d..32edc04e10f 100644
--- a/tests/classification/test_auroc.py
+++ b/tests/classification/test_auroc.py
@@ -188,3 +188,11 @@ def test_error_on_different_mode():
     with pytest.raises(ValueError, match=r"The mode of data.* should be constant.*"):
         # pass in multi-label data
         metric.update(torch.rand(10, 5), torch.randint(0, 2, (10, 5)))
+
+
+def test_error_multiclass_no_num_classes():
+    with pytest.raises(
+            ValueError,
+            match="Detected input to ``multiclass`` but you did not provide ``num_classes`` argument"
+    ):
+        _ = auroc(torch.randn(20, 3).softmax(dim=-1), torch.randint(3, (20, )))
diff --git a/torchmetrics/functional/classification/auroc.py b/torchmetrics/functional/classification/auroc.py
index c8a92737079..7dc0739f0fb 100644
--- a/torchmetrics/functional/classification/auroc.py
+++ b/torchmetrics/functional/classification/auroc.py
@@ -85,6 +85,8 @@ def _auroc_compute(
             fpr = [o[0] for o in output]
             tpr = [o[1] for o in output]
     else:
+        if mode != 'binary' and num_classes is None:
+            raise ValueError('Detected input to ``multiclass`` but you did not provide ``num_classes`` argument')
         fpr, tpr, _ = roc(preds, target, num_classes, pos_label, sample_weights)
 
     # calculate standard roc auc score