Granulation is a multivariable process characterized by several physical attributes that are essential for product performance, such as granule size and size distribution. An optimally operated granulation process will yield, in a reproducible manner, product with tightly controlled performance attributes. In this paper predictive models of the dynamics of these key variables are developed using a dynamic partial least squares approach. The method, demonstrated here on process simulation as well as on an industrial mixer-granulator process, result in accurate predictions. These models motivate the development of model predictive controllers for these processes.