diff --git a/pyopencl/array.py b/pyopencl/array.py
index fe96f91efb67ada5cb0daf5a8305291b6b08fdd5..42dc569bb73255b17fbab43b9263ca6a3462f910 100644
--- a/pyopencl/array.py
+++ b/pyopencl/array.py
@@ -400,6 +400,7 @@ class Array(object):
     .. automethod :: __ior__
 
     .. automethod :: __abs__
+    .. automethod :: __invert__
 
     .. UNDOC reverse()
 
@@ -885,6 +886,14 @@ class Array(object):
         return elementwise.get_array_binop_kernel(
                 out.context, op, out.dtype, a.dtype, b.dtype)
 
+    @staticmethod
+    @elwise_kernel_runner
+    def _unop(out, a, queue=None, op=None):
+        if out.shape != a.shape:
+            raise ValueError("shapes of arguments do not match")
+        return elementwise.get_unop_kernel(
+                out.context, op, a.dtype, out.dtype)
+
     # }}}
 
     # {{{ operators
@@ -1225,6 +1234,15 @@ class Array(object):
                 self._rpow_scalar(result, common_dtype.type(other), self))
         return result
 
+    def __invert__(self):
+        if not np.issubdtype(self.dtype, np.integer):
+            raise TypeError("Integral types only")
+
+        result = self._new_like_me()
+        result.add_event(self._unop(result, self, op="~"))
+
+        return result
+
     # }}}
 
     def reverse(self, queue=None):
diff --git a/pyopencl/elementwise.py b/pyopencl/elementwise.py
index b373d2fc2640715075ff6623b6cefa1326275b8a..f7d68eb0f4f78ce044cd307f368c379ac5ba670e 100644
--- a/pyopencl/elementwise.py
+++ b/pyopencl/elementwise.py
@@ -823,6 +823,16 @@ def get_pow_kernel(context, dtype_x, dtype_y, dtype_z,
             name="pow_method")
 
 
+@context_dependent_memoize
+def get_unop_kernel(context, operator, res_dtype, in_dtype):
+    return get_elwise_kernel(context, [
+        VectorArg(res_dtype, "z", with_offset=True),
+        VectorArg(in_dtype, "y", with_offset=True),
+        ],
+        "z[i] = %s y[i]" % operator,
+        name="unary_op_kernel")
+
+
 @context_dependent_memoize
 def get_array_scalar_binop_kernel(context, operator, dtype_res, dtype_a, dtype_b):
     return get_elwise_kernel(context, [
diff --git a/test/test_array.py b/test/test_array.py
index fd9a76e504f552bee14b32a50cf0eb906d64cbc9..045deb26b12770b0da396d8da8bac3c957a867c7 100644
--- a/test/test_array.py
+++ b/test/test_array.py
@@ -477,31 +477,25 @@ def test_bitwise(ctx_factory):
 
     dtypes = [np.dtype(t) for t in (np.int64, np.int32, np.int16, np.int8)]
 
+    from pyopencl.clrandom import rand as clrand
+
     for a_dtype, b_dtype in product(dtypes, dtypes):
         l = 16
 
         np.random.seed(10)
 
-        a_dev = cl.array.to_device(
-            queue,
-            np.random.randint(
-                low=np.iinfo(a_dtype).min,
-                high=1+np.iinfo(a_dtype).max,
-                size=(l,),
-                dtype=a_dtype))
-        b_dev = cl.array.to_device(
-            queue,
-            np.random.randint(
-                low=np.iinfo(b_dtype).min,
-                high=1+np.iinfo(b_dtype).max,
-                size=(l,),
-                dtype=b_dtype))
+        int32_min = np.iinfo(np.int32).min
+        int32_max = np.iinfo(np.int32).max
+
+        a_dev = clrand(
+            queue, (l,), a=int32_min, b=1+int32_max, dtype=np.int64).astype(a_dtype)
+        b_dev = clrand(
+            queue, (l,), a=int32_min, b=1+int32_max, dtype=np.int64).astype(b_dtype)
 
         a = a_dev.get()
         b = b_dev.get()
-        s = np.random.randint(
-            low=np.iinfo(b_dtype).min,
-            high=1+np.iinfo(b_dtype).max)
+        s = int((clrand(queue, (), a=int32_min, b=1+int32_max, dtype=np.int64)
+                 .astype(b_dtype).get()))
 
         import operator as o
 
@@ -536,6 +530,11 @@ def test_bitwise(ctx_factory):
 
             assert (res_dev.get() == res).all()
 
+        # Test unary ~
+        res_dev = ~a_dev
+        res = ~a
+        assert (res_dev.get() == res).all()
+
 # }}}