This paper compares radial basis function networks for identification of nonlinear dynamic systems with classical methods derived from the Volterra series. The performance of these different approaches as Hammerstein, Wiener and NDE models is analysed. Since the centres and variances of the Gaussian radial basis functions will be fixed before learning and only the weights are learned, a linear optimization problem arises. Therefore training the network and parameter estimation become comparable in calculational effort.It will be shown, that the classical methods can compete or even perform better than the neural network, if the assumptions for the structure are valid. However if in practical applications the structure is not known the radial basis function network performs much better than the classical methods.