A technique for the modelling of nonlinear systems using a fuzzy neural network topology is described. The input space of a nonlinear system is initially divided into a number of fuzzy operating regions within which reduced order models are able to represent the system. The complete system model output, the global model, is obtained through the conjunction of the outputs of the local models. The fuzzy neural network approach to nonlinear process modelling provides a way of opening up the purely 'black box' approach normally seen in neural network applications. Process knowledge is used to identify appropriate local operating regions and as an aid to initialising the network structure. Fuzzy neural network models are also easier to interpret than conventional neural network models. The weights in a trained fuzzy network model can be interpreted in terms of process information. This technique has been applied to model the nonlinear dynamic behaviour of a pH reactor and two static nonlinear systems. Correlation based tests are used to assess the fuzzy network model validity for nonlinear dynamic systems.