The number of size classes in a cumulative rates model of a grinding mill circuit is reduced to determine the minimum number required to provide a reasonably accurate model of the circuit for process control. Each reduced size class set is used to create a non-linear cumulative rates model which is linearized to design a linear model predictive controller. The accuracy of a model is determined by the ability of the corresponding model predictive controller to control important process variables in the grinding mill circuit as represented by the full non-linear cumulative rates model. Results show that a model with 25 size classes that provides valuable information for plant design and scale-up, can be reduced to a model containing only a small number of size class sets and still be suitable for process control. Although as few as 3 size classes can be used to obtain a fairly accurate model for process control, the distribution of these 3 size classes influences the accuracy of the model. For a model to be useful for process control, the model should at least provide the directions in which the process variables change. (C) 2013 Elsevier B.V. All rights reserved.