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There has been considerable interest and activity in the area of application of the artificial neural network (ANN) to hydraulic systems. The pattern recognition capabilities of the ANN has led to an early investigation in areas where the neural networks could be trained using signals that were at least statistically similar to those signals which the trained ANN would be exposed during operation. The dynamic and encompassing nature of hydraulic system signals poses more of a challenge to ANN training and implementation than one of only pattern recognition. However, in the past decade, there has been considerable activity and progress in the application of ANN techniques for hydraulic systems control, identification and condition monitoring. This paper provides an overview of work in this area. The ANN has proven to be a valuable addition to the current existing techniques.