diff --git a/pylops_mpi/DistributedArray.py b/pylops_mpi/DistributedArray.py
index 6e5a471..0b2e2c6 100644
--- a/pylops_mpi/DistributedArray.py
+++ b/pylops_mpi/DistributedArray.py
@@ -5,6 +5,7 @@
 from enum import Enum
 
 from pylops.utils import DTypeLike, NDArray
+from pylops.utils._internal import _value_or_sized_to_tuple
 from pylops.utils.backend import get_module, get_array_module, get_module_name
 
 
@@ -78,7 +79,7 @@ class DistributedArray:
     axis : :obj:`int`, optional
         Axis along which distribution occurs. Defaults to ``0``.
     local_shapes : :obj:`list`, optional
-        List of tuples representing local shapes at each rank.
+        List of tuples or integers representing local shapes at each rank.
     engine : :obj:`str`, optional
         Engine used to store array (``numpy`` or ``cupy``)
     dtype : :obj:`str`, optional
@@ -88,7 +89,7 @@ class DistributedArray:
     def __init__(self, global_shape: Union[Tuple, Integral],
                  base_comm: Optional[MPI.Comm] = MPI.COMM_WORLD,
                  partition: Partition = Partition.SCATTER, axis: int = 0,
-                 local_shapes: Optional[List[Tuple]] = None,
+                 local_shapes: Optional[List[Union[Tuple, Integral]]] = None,
                  engine: Optional[str] = "numpy",
                  dtype: Optional[DTypeLike] = np.float64):
         if isinstance(global_shape, Integral):
@@ -100,10 +101,12 @@ def __init__(self, global_shape: Union[Tuple, Integral],
             raise ValueError(f"Should be either {Partition.BROADCAST} "
                              f"or {Partition.SCATTER}")
         self.dtype = dtype
-        self._global_shape = global_shape
+        self._global_shape = _value_or_sized_to_tuple(global_shape)
         self._base_comm = base_comm
         self._partition = partition
         self._axis = axis
+
+        local_shapes = local_shapes if local_shapes is None else [_value_or_sized_to_tuple(local_shape) for local_shape in local_shapes]
         self._check_local_shapes(local_shapes)
         self._local_shape = local_shapes[base_comm.rank] if local_shapes else local_split(global_shape, base_comm,
                                                                                           partition, axis)