diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 34a437ba40bd8..8918f488e4e04 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -1609,9 +1609,18 @@ def _validate_key(self, key, axis: AxisInt) -> None:
             if not is_numeric_dtype(arr.dtype):
                 raise IndexError(f".iloc requires numeric indexers, got {arr}")
 
-            # check that the key does not exceed the maximum size of the index
-            if len(arr) and (arr.max() >= len_axis or arr.min() < -len_axis):
-                raise IndexError("positional indexers are out-of-bounds")
+            if len(arr):
+                # handle ExtensionArray using _reduce method else use numpy
+                if isinstance(arr.dtype, ExtensionDtype):
+                    arr_max = arr._reduce("max")
+                    arr_min = arr._reduce("min")
+                else:
+                    arr_max = np.max(arr)
+                    arr_min = np.min(arr)
+
+                # check that the key does not exceed the maximum size
+                if arr_max >= len_axis or arr_min < -len_axis:
+                    raise IndexError("positional indexers are out-of-bounds")
         else:
             raise ValueError(f"Can only index by location with a [{self._valid_types}]")
 
diff --git a/pandas/tests/indexing/test_iloc.py b/pandas/tests/indexing/test_iloc.py
index 3be69617cad43..71e7c581c2d17 100644
--- a/pandas/tests/indexing/test_iloc.py
+++ b/pandas/tests/indexing/test_iloc.py
@@ -1478,3 +1478,14 @@ def test_iloc_nullable_int64_size_1_nan(self):
         result = DataFrame({"a": ["test"], "b": [np.nan]})
         with pytest.raises(TypeError, match="Invalid value"):
             result.loc[:, "b"] = result.loc[:, "b"].astype("Int64")
+
+    def test_iloc_arrow_extension_array(self):
+        # GH#61311
+        pytest.importorskip("pyarrow")
+        df = DataFrame({"a": [1, 2], "c": [0, 2], "d": ["c", "a"]})
+        df_arrow = DataFrame(
+            {"a": [1, 2], "c": [0, 2], "d": ["c", "a"]}
+        ).convert_dtypes(dtype_backend="pyarrow")
+        expected = df.iloc[:, df["c"]]
+        result = df_arrow.iloc[:, df_arrow["c"]]
+        tm.assert_frame_equal(result, expected, check_dtype=False)