Although nonlinear inverse and predictive control techniques based on artificial neural networks have been extensively applied to nonlinear systems, their use in real time applications is generally limited. In this paper neural inverse and predictive control systems have been applied to the real-time control of the heat transfer fluid temperature in a pilot chemical reactor. The training of the inverse control system is carried out using both generalised and specialised learning. This allows the preparation of weights of the controller acting in real-time and appropriate performances of inverse neural controller can be achieved. The predictive control system makes use of a neural network to calculate the control action. Thus, the problems related to the high computational effort involved in nonlinear model-predictive control systems are reduced. The performance of the neural controllers is compared against the self-tuning PID controller currently installed in the plant. The results show that neural-based controllers improve the performance of the real plant.