The success of model based control of chemical processes is dependent on good process models. Many of these processes exhibit strong nonlinearity and time varying parameters and are often difficult to model accurately, The 'grey-box' model which combines partial knowledge of the process, with a neural network to capture the remaining dynamics, is a promising modelling tool for nonlinear processes. This modelling methodology maximizes the use of a priori process knowledge. This, in turn, reduces the size of the neural network required to capture the remaining dynamics, hence, less data for training and faster convergence can be achieved. The grey-box model is combined with a generic model control structure and applied to a number of simulations as well as a real-time process.