Granulation is a complex multivariable process with significant industrial importance. In this paper, a dynamic partial least-squares (PLS) approach is used to develop empirical predictive models of key process variables. PLS is tested on a detailed process simulator as well as on an industrial mixer-granulator process. The applicability of the nonlinear dynamic kernel-PLS (KPLS) for granulation process is also demonstrated. Accurate predictions obtained by these methods motivate the development of model predictive controllers for these processes.