diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 6903deb7..1be8bb8d 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -17,6 +17,7 @@ foreach(execid
   conv2d_network
   optimizers
   loss
+  metrics
   )
   add_executable(test_${execid} test_${execid}.f90)
   target_link_libraries(test_${execid} PRIVATE neural-fortran h5fortran::h5fortran jsonfortran::jsonfortran ${LIBS})
diff --git a/test/test_metrics.f90 b/test/test_metrics.f90
new file mode 100644
index 00000000..a9db74ed
--- /dev/null
+++ b/test/test_metrics.f90
@@ -0,0 +1,70 @@
+program test_metrics
+  use iso_fortran_env, only: stderr => error_unit
+  use nf, only: dense, input, network, sgd, mse
+  implicit none
+  type(network) :: net
+  logical :: ok = .true.
+
+  ! Minimal 2-layer network
+  net = network([ &
+    input(1), &
+    dense(1) &
+  ])
+
+  training: block
+    real :: x(1), y(1)
+    real :: tolerance = 1e-3
+    integer :: n
+    integer, parameter :: num_iterations = 1000 
+    real :: quadratic_loss, mse_metric
+    real, allocatable :: metrics(:,:)
+
+    x = [0.1234567]
+    y = [0.7654321]
+
+    do n = 1, num_iterations
+      call net % forward(x)
+      call net % backward(y)
+      call net % update(sgd(learning_rate=1.))
+      if (all(abs(net % predict(x) - y) < tolerance)) exit
+    end do
+    
+    ! Returns only one metric, based on the default loss function (quadratic).
+    metrics = net % evaluate(reshape(x, [1, 1]), reshape(y, [1, 1]))
+    quadratic_loss = metrics(1,1)
+
+    if (.not. all(shape(metrics) == [1, 1])) then
+      write(stderr, '(a)') 'metrics array is the correct shape (1, 1).. failed'
+      ok = .false.
+    end if
+
+    ! Returns two metrics, one from the loss function and another specified by the user.
+    metrics = net % evaluate(reshape(x, [1, 1]), reshape(y, [1, 1]), metric=mse())
+
+    if (.not. all(shape(metrics) == [1, 2])) then
+      write(stderr, '(a)') 'metrics array is the correct shape (1, 2).. failed'
+      ok = .false.
+    end if
+
+    mse_metric = metrics(1,2)
+
+    if (.not. all(metrics < 1e-5)) then
+      write(stderr, '(a)') 'value for all metrics is expected.. failed'
+      ok = .false.
+    end if
+
+    if (.not. metrics(1,1) == quadratic_loss) then
+      write(stderr, '(a)') 'first metric should be the same as that of the loss function.. failed'
+      ok = .false.
+    end if
+
+  end block training
+
+  if (ok) then
+    print '(a)', 'test_metrics: All tests passed.'
+  else
+    write(stderr, '(a)') 'test_metrics: One or more tests failed.'
+    stop 1
+  end if
+
+end program test_metrics