We present a technique for nonlinear system identification and model reduction using artificial neural networks (ANNs). The ANN is used to model plant input-output data, with the states of the model being represented by the outputs of an intermediate hidden layer of the ANN. Model reduction is achieved by applying a singular value decomposition (SVD)-based technique to the weight matrices of the ANN. The sequence of state values is used to convert the model to a form that is useful for state and parameter estimation. Examples of chemical systems (batch and continuous reactors and distillation columns) are presented to demonstrate the performance of the ANN-based system identification and model reduction technique. (C) 2003 Elsevier Ltd. All rights reserved.