diff --git a/CHANGELOG.md b/CHANGELOG.md
index 37ea979835..ebe3a2d5b5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -27,6 +27,8 @@
 - [#840](https://github.com/helmholtz-analytics/heat/pull/840) New feature: `vecdot()`
 - [#846](https://github.com/helmholtz-analytics/heat/pull/846) New features `norm`, `vector_norm`, `matrix_norm`
 - [#850](https://github.com/helmholtz-analytics/heat/pull/850) New Feature `cross`
+- [#877](https://github.com/helmholtz-analytics/heat/pull/877) New feature `det`
+
 ### Logical
 - [#862](https://github.com/helmholtz-analytics/heat/pull/862) New feature `signbit`
 ### Manipulations
diff --git a/heat/core/dndarray.py b/heat/core/dndarray.py
index d968ce2fe6..2580d09090 100644
--- a/heat/core/dndarray.py
+++ b/heat/core/dndarray.py
@@ -65,7 +65,7 @@ def __init__(
         array: torch.Tensor,
         gshape: Tuple[int, ...],
         dtype: datatype,
-        split: int,
+        split: Union[int, None],
         device: Device,
         comm: Communication,
         balanced: bool,
diff --git a/heat/core/linalg/basics.py b/heat/core/linalg/basics.py
index 39589f6967..a1d339af35 100644
--- a/heat/core/linalg/basics.py
+++ b/heat/core/linalg/basics.py
@@ -26,6 +26,7 @@
 
 __all__ = [
     "cross",
+    "det",
     "dot",
     "matmul",
     "matrix_norm",
@@ -155,6 +156,92 @@ def cross(
     return ret
 
 
+def det(a: DNDarray) -> DNDarray:
+    """
+    Returns the determinant of a square matrix.
+
+    Parameters
+    ----------
+    a : DNDarray
+        A square matrix or a stack of matrices. Shape = (...,M,M)
+
+    Raises
+    ------
+    RuntimeError
+        If the dtype of 'a' is not floating-point.
+    RuntimeError
+        If `a.ndim < 2` or if the length of the last two dimensions is not the same.
+
+    Examples
+    --------
+    >>> a = ht.array([[-2,-1,2],[2,1,4],[-3,3,-1]])
+    >>> ht.linalg.det(a)
+    DNDarray(54., dtype=ht.float64, device=cpu:0, split=None)
+    """
+    sanitation.sanitize_in(a)  # pragma: no cover
+
+    if a.ndim < 2:
+        raise RuntimeError("DNDarray must be at least two-dimensional.")
+
+    m, n = a.shape[-2:]
+    if m != n:
+        raise RuntimeError("Last two dimensions of the DNDarray must be square.")
+
+    if types.heat_type_is_exact(a.dtype):
+        raise RuntimeError("dtype of DNDarray must be floating-point.")
+
+    # no split in the square matrices
+    if not a.is_distributed() or a.split < a.ndim - 2:
+        data = torch.linalg.det(a.larray)
+        sp = None if not a.is_distributed() else a.split
+        return DNDarray(
+            data,
+            a.shape[:-2],
+            types.heat_type_of(data),
+            split=sp,
+            device=a.device,
+            comm=a.comm,
+            balanced=a.balanced,
+        )
+
+    acopy = a.copy()
+    acopy = manipulations.reshape(acopy, (-1, m, m), new_split=a.split - a.ndim + 3)
+    adet = factories.ones(acopy.shape[0], dtype=a.dtype, device=a.device)
+
+    for k in range(adet.shape[0]):
+        m = 0
+        for i in range(n):
+            # partial pivoting
+            if np.isclose(acopy[k, i, i].item(), 0):
+                abord = True
+                for j in range(i + 1, n):
+                    if not np.isclose(acopy[k, j, i].item(), 0):
+                        if a.split == a.ndim - 2:  # split=0 on square matrix
+                            acopy[k, i, :], acopy[k, j, :] = acopy[k, j, :], acopy[k, i, :].copy()
+                        else:  # split=1
+                            acopy.larray[k, i, :], acopy.larray[k, j, :] = (
+                                acopy.larray[k, j, :],
+                                acopy.larray[k, i, :].clone(),
+                            )
+                        abord = False
+                        m += 1
+                        break
+                if abord:
+                    adet[k] = 0
+                    break
+
+            adet[k] *= acopy[k, i, i]
+            z = acopy[k, i + 1 :, i, None].larray / acopy[k, i, i].item()
+            acopy[k, i + 1 :, :].larray -= z * acopy[k, i, :].larray
+
+        if m % 2 != 0:
+            adet[k] = -adet[k]
+
+    adet = manipulations.reshape(adet, a.shape[:-2])
+
+    return adet
+
+
 def dot(a: DNDarray, b: DNDarray, out: Optional[DNDarray] = None) -> Union[DNDarray, float]:
     """
     Returns the dot product of two ``DNDarrays``.
diff --git a/heat/core/linalg/tests/test_basics.py b/heat/core/linalg/tests/test_basics.py
index ff034da3af..fd32f99502 100644
--- a/heat/core/linalg/tests/test_basics.py
+++ b/heat/core/linalg/tests/test_basics.py
@@ -90,6 +90,84 @@ def test_cross(self):
         with self.assertRaises(ValueError):
             ht.cross(ht.eye(3, split=0), ht.eye(3, split=0), axis=0)
 
+    def test_det(self):
+        # (3,3) with pivoting
+        ares = ht.array(54.0)
+        a = ht.array([[-2.0, -1, 2], [2, 1, 4], [-3, 3, -1]], split=0, dtype=ht.double)
+        adet = ht.linalg.det(a)
+
+        self.assertTupleEqual(adet.shape, ares.shape)
+        self.assertIsNone(adet.split)
+        self.assertEqual(adet.dtype, a.dtype)
+        self.assertEqual(adet.device, a.device)
+        self.assertTrue(ht.equal(adet, ares))
+
+        a = ht.array([[-2.0, -1, 2], [2, 1, 4], [-3, 3, -1]], split=1, dtype=ht.double)
+        adet = ht.linalg.det(a)
+
+        self.assertTupleEqual(adet.shape, ares.shape)
+        self.assertIsNone(adet.split)
+        self.assertEqual(adet.dtype, a.dtype)
+        self.assertEqual(adet.device, a.device)
+        self.assertTrue(ht.equal(adet, ares))
+
+        # det==0
+        ares = ht.array(0.0)
+        a = ht.array([[0, 0, 0], [2, 1, 4], [-3, 3, -1]], dtype=ht.float64, split=0)
+        adet = ht.linalg.det(a)
+
+        self.assertTupleEqual(adet.shape, ares.shape)
+        self.assertIsNone(adet.split)
+        self.assertEqual(adet.dtype, a.dtype)
+        self.assertEqual(adet.device, a.device)
+        self.assertTrue(ht.equal(adet, ares))
+
+        # (3,2,2)
+        ares = ht.array([-2.0, -3.0, -8.0])
+
+        a = ht.array([[[1.0, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]])
+        adet = ht.linalg.det(a)
+
+        self.assertTupleEqual(adet.shape, ares.shape)
+        self.assertIsNone(adet.split)
+        self.assertEqual(adet.dtype, a.dtype)
+        self.assertEqual(adet.device, a.device)
+        self.assertTrue(ht.allclose(adet, ares))
+
+        a = ht.array([[[1.0, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]], split=0)
+        adet = ht.linalg.det(a)
+
+        self.assertTupleEqual(adet.shape, ares.shape)
+        self.assertEqual(adet.split, a.split if a.is_distributed() else None)
+        self.assertEqual(adet.dtype, a.dtype)
+        self.assertEqual(adet.device, a.device)
+        self.assertTrue(ht.allclose(adet, ares))
+
+        a = ht.array([[[1.0, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]], split=1)
+        adet = ht.linalg.det(a)
+
+        self.assertTupleEqual(adet.shape, ares.shape)
+        self.assertIsNone(adet.split)
+        self.assertEqual(adet.dtype, a.dtype)
+        self.assertEqual(adet.device, a.device)
+        self.assertTrue(ht.allclose(adet, ares))
+
+        a = ht.array([[[1.0, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]], split=2)
+        adet = ht.linalg.det(a)
+
+        self.assertTupleEqual(adet.shape, ares.shape)
+        self.assertIsNone(adet.split)
+        self.assertEqual(adet.dtype, a.dtype)
+        self.assertEqual(adet.device, a.device)
+        self.assertTrue(ht.allclose(adet, ares))
+
+        with self.assertRaises(RuntimeError):
+            ht.linalg.det(ht.array([1, 2, 3], split=0))
+        with self.assertRaises(RuntimeError):
+            ht.linalg.det(ht.zeros((2, 2, 3), split=1))
+        with self.assertRaises(RuntimeError):
+            ht.linalg.det(ht.zeros((2, 2), dtype=ht.int, split=0))
+
     def test_dot(self):
         # ONLY TESTING CORRECTNESS! ALL CALLS IN DOT ARE PREVIOUSLY TESTED
         # cases to test: