Granulation is a complex process in which many input variables influence many product properties. As Iveson et al. describe in a review paper (2001), the understanding of the fundamental processes that control granulation behavior and product properties have increased in recent years. This knowledge can be used during process design, in choosing the right formulation and operating conditions, and it can also be used to improve process control. Although many variables are set constant during process design, variations during production in input variables occur due to the variable nature of the powder feed. Even if all granule properties, except for size, are ignored for process control, a one dimensional granule size distribution can be constructed by multiple discrete output variables, in order to represent the shape of the distribution (these can be mean sizes (with coefficients of variation), percentile sizes, moments 4 or size bins). Model Predictive Control (MPC) is an effective method to control such multiple input, multiple output processes (García, et al., 1989). More details motivating the choice of MPC for granulation processes along with examples from the literature can be found in previous reports (IFPRI# ARR51-02 and IFPRI# ARR51-03).