diff --git a/doc/source/whatsnew/v1.2.0.rst b/doc/source/whatsnew/v1.2.0.rst
index 2a8b6fe3ade6a..3c020e7c20876 100644
--- a/doc/source/whatsnew/v1.2.0.rst
+++ b/doc/source/whatsnew/v1.2.0.rst
@@ -240,7 +240,7 @@ Bug fixes
 Categorical
 ^^^^^^^^^^^
 - :meth:`Categorical.fillna` will always return a copy, will validate a passed fill value regardless of whether there are any NAs to fill, and will disallow a ``NaT`` as a fill value for numeric categories (:issue:`36530`)
--
+- Bug in :meth:`Categorical.__setitem__` that incorrectly raised when trying to set a tuple value (:issue:`20439`)
 -
 
 Datetimelike
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index d2f88b353e1c1..1902d3ec1f9cc 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -28,6 +28,7 @@
     is_dict_like,
     is_dtype_equal,
     is_extension_array_dtype,
+    is_hashable,
     is_integer_dtype,
     is_list_like,
     is_object_dtype,
@@ -61,8 +62,9 @@ def _cat_compare_op(op):
 
     @unpack_zerodim_and_defer(opname)
     def func(self, other):
-        if is_list_like(other) and len(other) != len(self):
-            # TODO: Could this fail if the categories are listlike objects?
+        hashable = is_hashable(other)
+        if is_list_like(other) and len(other) != len(self) and not hashable:
+            # in hashable case we may have a tuple that is itself a category
             raise ValueError("Lengths must match.")
 
         if not self.ordered:
@@ -90,7 +92,7 @@ def func(self, other):
                 ret[mask] = fill_value
             return ret
 
-        if is_scalar(other):
+        if hashable:
             if other in self.categories:
                 i = self._unbox_scalar(other)
                 ret = op(self._codes, i)
@@ -1883,7 +1885,8 @@ def _validate_setitem_value(self, value):
             new_codes = self._validate_listlike(value)
             value = Categorical.from_codes(new_codes, dtype=self.dtype)
 
-        rvalue = value if is_list_like(value) else [value]
+        # wrap scalars and hashable-listlikes in list
+        rvalue = value if not is_hashable(value) else [value]
 
         from pandas import Index
 
diff --git a/pandas/tests/arrays/categorical/test_indexing.py b/pandas/tests/arrays/categorical/test_indexing.py
index ab8606ef9258d..2c4dd8fe64057 100644
--- a/pandas/tests/arrays/categorical/test_indexing.py
+++ b/pandas/tests/arrays/categorical/test_indexing.py
@@ -75,7 +75,7 @@ def test_setitem_different_unordered_raises(self, other):
             pd.Categorical(["b", "a"], categories=["a", "b", "c"], ordered=True),
         ],
     )
-    def test_setitem_same_ordered_rasies(self, other):
+    def test_setitem_same_ordered_raises(self, other):
         # Gh-24142
         target = pd.Categorical(["a", "b"], categories=["a", "b"], ordered=True)
         mask = np.array([True, False])
@@ -83,6 +83,14 @@ def test_setitem_same_ordered_rasies(self, other):
         with pytest.raises(ValueError, match=msg):
             target[mask] = other[mask]
 
+    def test_setitem_tuple(self):
+        # GH#20439
+        cat = pd.Categorical([(0, 1), (0, 2), (0, 1)])
+
+        # This should not raise
+        cat[1] = cat[0]
+        assert cat[1] == (0, 1)
+
 
 class TestCategoricalIndexing:
     def test_getitem_slice(self):
diff --git a/pandas/tests/arrays/categorical/test_operators.py b/pandas/tests/arrays/categorical/test_operators.py
index 34194738bf4ab..ed70417523491 100644
--- a/pandas/tests/arrays/categorical/test_operators.py
+++ b/pandas/tests/arrays/categorical/test_operators.py
@@ -179,6 +179,20 @@ def test_comparison_with_unknown_scalars(self):
         tm.assert_numpy_array_equal(cat == 4, np.array([False, False, False]))
         tm.assert_numpy_array_equal(cat != 4, np.array([True, True, True]))
 
+    def test_comparison_with_tuple(self):
+        cat = pd.Categorical(np.array(["foo", (0, 1), 3, (0, 1)], dtype=object))
+
+        result = cat == "foo"
+        expected = np.array([True, False, False, False], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat == (0, 1)
+        expected = np.array([False, True, False, True], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat != (0, 1)
+        tm.assert_numpy_array_equal(result, ~expected)
+
     def test_comparison_of_ordered_categorical_with_nan_to_scalar(
         self, compare_operators_no_eq_ne
     ):