This paper describes the collaborative results of a study between The University of Newcastle, Sydney University, ICI Engineering Technology and ICI Australia Pty Ltd into the application of neural networks to Model Based Predictive Control. The results discussed will describe the methodology of extracting data from a real industrial process, pre-processing the data, selection of key inputs using dynamic correlation and multivariate statistics, process modelling and control. The implementation of the controller was carried out on a validated simulation of the actual process. This Speedup model had been developed by Sydney University over a period of eighteen months and had been used previously to design other successful control strategies that are now on-line on the process. The neural network models were generated over a period of 12 man weeks and could calculate 1000 predictions in a few seconds as opposed to over 12 hours from the SpeedUp simulation. The resultant Model-Based controller was benchmarked against a linear model based controller and two PID controllers. The neural network controller not only outperformed the linear MBPC by a 50% reduction in standard deviation but also reduced overshoot and settling time dramatically.