Neural networks have been widely used to model nonlinear systems for control. The curse of dimensionality and lack of transparency of such neural network models has forced a shift towards local model networks and recently towards the nonparametric Gaussian processes approach. Assuming common validity functions, all of these models have a similar structure. This paper examines the evolution from the radial basis function network to the local model network and finally to the Gaussian process model. A simulated example is used to explain the advantages and disadvantages of each structure. (C) 2007 Elsevier Ltd. All rights reserved.