Neural networks are currently finding practical applications ranging from ‘soft’ regulatory control in consumer products to accurate control of non-linear plant in the process industries. This paper describes the application of neural networks to modelling and control of a prototype underwater vehicle, as an example of a system containing severe non-linearities. The most common implementation strategy for neural control is model predictive control, where a model of the process is developed first and is used off-line to design an appropriate compensator. The accuracy and robustness of this control strategy relies on the quality of the non-linear process model, in particular its ability to predict the plant response accurately multiple-steps ahead. In this paper, several neural network architectures are used to evaluate a long-range model predictive control structure, both in simulation and for on-line control of vehicle depth, achieving accurate control with a smooth actuator signal.