Fluidized catalytic cracking (FCC) is an important process in petroleum refining. This process needs to be maintained close to optimum operating conditions because of fiscal incentives. Methods such as dynamic matrix control have been used for control of this process. However, the constrained optimization problem involved in the control has generally been solved in a piecemeal fashion. For improved control, the optimization problem needs to be solved as a whole, that is, without decomposition. In this paper, a linear programming formulation using a simplified model predictive control algorithm is presented. The performance of the formulation is tested on a mathematical model of the FCC process for set-point changes, disturbance rejection, station failure, and model mismatch. The results show that an improved control performance can be obtained by including the constraints within the optimization problem.