diff --git a/doc/source/whatsnew/v1.3.0.rst b/doc/source/whatsnew/v1.3.0.rst
index 1dc635bebe731..bf63a51204f5c 100644
--- a/doc/source/whatsnew/v1.3.0.rst
+++ b/doc/source/whatsnew/v1.3.0.rst
@@ -221,6 +221,7 @@ Other enhancements
 - :meth:`pandas.read_csv` and :meth:`pandas.read_json` expose the argument ``encoding_errors`` to control how encoding errors are handled (:issue:`39450`)
 - :meth:`.GroupBy.any` and :meth:`.GroupBy.all` use Kleene logic with nullable data types (:issue:`37506`)
 - :meth:`.GroupBy.any` and :meth:`.GroupBy.all` return a ``BooleanDtype`` for columns with nullable data types (:issue:`33449`)
+- Add keyword ``sort`` to :func:`pivot_table` to allow non-sorting of the result (:issue:`39143`)
 -
 
 .. ---------------------------------------------------------------------------
diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py
index 4c73e5d594d2b..51556fda6da04 100644
--- a/pandas/core/reshape/pivot.py
+++ b/pandas/core/reshape/pivot.py
@@ -64,6 +64,7 @@ def pivot_table(
     dropna=True,
     margins_name="All",
     observed=False,
+    sort=True,
 ) -> DataFrame:
     index = _convert_by(index)
     columns = _convert_by(columns)
@@ -83,6 +84,7 @@ def pivot_table(
                 dropna=dropna,
                 margins_name=margins_name,
                 observed=observed,
+                sort=sort,
             )
             pieces.append(_table)
             keys.append(getattr(func, "__name__", func))
@@ -101,6 +103,7 @@ def pivot_table(
         dropna,
         margins_name,
         observed,
+        sort,
     )
     return table.__finalize__(data, method="pivot_table")
 
@@ -116,6 +119,7 @@ def __internal_pivot_table(
     dropna: bool,
     margins_name: str,
     observed: bool,
+    sort: bool,
 ) -> DataFrame:
     """
     Helper of :func:`pandas.pivot_table` for any non-list ``aggfunc``.
@@ -157,7 +161,7 @@ def __internal_pivot_table(
                 pass
         values = list(values)
 
-    grouped = data.groupby(keys, observed=observed)
+    grouped = data.groupby(keys, observed=observed, sort=sort)
     agged = grouped.agg(aggfunc)
     if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):
         agged = agged.dropna(how="all")