This paper illustrates the benefits of a nonlinear model based predictive control (NMPC) strategy for set-point tracking control of an industrial crystallization process. A neural networks model is used as internal model to predict process outputs. An optimization problem is solved to compute future control actions taking into account real-time control objectives. Furthermore, a more suitable output variable is used for process control: the mass of crystals in the solution is used instead of the traditional electrical conductivity. The performance of the NMPC implementation is assessed via simulation results based on industrial data. (C) 2010 Elsevier Ltd. All rights reserved.