This paper summarises a 2-year industrial investigation into the application of artificial neural networks in the area of process monitoring and control. The investigation was a collaborative programme between the University of Newcastle-upon-Tyne, EDS and 24 UK based international companies. Descriptions of the major activities undertaken in this programme, which included the application of neural networks for fault detection in a vitrification process and the model based predictive control of a gasoline engine are provided. The paper also describes some of the practical difficulties that were experienced while applying neural networks and lists the important lessons that were learned through the completion of this project. The main conclusion from the work was that neural networks are capable of improving industrial process monitoring and control systems. However. the level of improvement must be analysed on a problem specific basis and in many applications the use of neural networks may not be justified.