This paper addresses the use of feedforward neural networks for the steady-state and dynamic identification and control of a riser type fluid catalytic cracking unit (FCCU). The results are compared with a conventional PI controller and a model predictive control (MPC) using a state space subspace identification algorithm. A back propagation algorithm with momentum term and adaptive learning rate is used for training the identification networks. The back propagation algorithm is also used for the neuro-control of the process. It is shown that for a noise-free system the adaptive neuro-controller and the MPC are capable of maintaining the riser temperature, the pressure difference between the reactor vessel and the regenerator, and the catalyst bed level in the reactor vessel, in the presence of set-point and disturbance changes. The MPC performs better than the neuro controller that in turn is superior to the conventional multi-loop diagonal PI controller.