Fuzzy networks and neural networks offer two different approaches of nonlinear black box modelling. Efficient identification methods have been developed to calculate the parameters for a given structure and have been applied successfully in many examples. But the applications proposed in the literature usually miss the comparison of the alternative method, so that the selection of the more suitable approach for a given task is difficult. This paper aims to ease the decision for one of the two methodologies by considering one well-known high quality approximator of each network type, and presenting a fair comparison. For this purpose, two mathematical and three complex technical examples of nonlinear systems are considered. Generally, fuzzy networks and neural networks face the problem of overtraining causing poor validation/generalisation results. A modification of the established identification methods is proposed as a significant improvement for both approaches.