A new algorithm for training radial basis function neural networks is presented in this paper. The algorithm, which is based on the subtractive clustering technique, has a number of advantages compared to the traditional learning algorithms, including faster training times and more accurate predictions. Due to these advantages the method proves suitable for developing discrete-time models for complex dynamical systems.