The goal of this paper is to discuss a neural network modeling approach for developing a quantitatively good model for proton exchange membrane (PEM) fuel cells. Various ANN approaches have been tested; the back-propagation feed-forward networks and radial basis function networks show satisfactory performance with regard to cell voltage prediction. The effects of Pi loading on the performance of the PEM fuel cell have been specifically studied. The results show that the ANN model is capable of simulating these effects for which there are. currently no valid fundamental models available from the open literature. Two novel hybrid neural network models (multiplicative and additive), each consisting of an ANN component and a physical component. have been developed and compared with the full-blown ANN model. The results from the hybrid models demonstrate comparable performance (in terms of cell voltage predictions) compared to the ANN model. Additionally, the hybrid models show performance gains over the physical model alone. The additive hybrid model shows better accuracy than that of the multiplicative hybrid model in our tests. (C) 2004 Elsevier B.V. All rights reserved.