Learning for feedforward neural networks can be regarded as a nonlinear parameter estimation problem with the objective of finding the optimal weights that provide the best fitting of a given training set. The extended Kalman filter is well-suited to accomplishing this task, as it is a recursive state estimation method for nonlinear systems. Such a training can be performed also in batch mode. In this paper the algorithm is coded in an efficient way and its performance is compared with a variety of widespread training methods. Simulation results show that the latter are outperformed by EKF-based parameters optimization.