Catalytic naphtha reforming is an important process carried out in refineries for upgrading low-octane naphtha to high-octane gasoline. A reformer can meet many product demands through its wide range of design and flexibility of operation. This work deals with optimization of a catalytic reformer using artificial neural networks (ANN). This optimization requires an accurate process model that is valid over wide range of operating conditions. In this work, a simple kinetic model has been developed. This model gives the temperature and concentration profiles of three important hydrocarbons (naphthenes, paraffins, and aromatics) across the reactors. An optimal control scheme using artificial neural networks has been developed to maximize the aromatics yield, subject to constraints in inlet temperature of the reactors. Two neural networks, one in the forward path and the other in the feedback path, are trained to give set points for temperature control loops of the three reactors. Finally, the results are compared with conventional aromatics yield, and the best performance of ANN based control is validated.