This paper provides a discussion on the modeling of granular mixing using Markov chain theory. Previous papers on this topic are either based on restrictive underlying assumptions about the flow structure or are limited to a small number of states. In this paper, a generalized approach for the construction of a multidimensional state space first order Markov chain that represents the mixing of monodisperse particles is introduced. The transition probability matrix is computed directly using results obtained from a discrete element model. This work shows that, if accurate measurements of the state of the system are available, the associated Markov operator leads to a good estimate of the particle dynamics in the system. (C) 2007 Elsevier Ltd. All rights reserved.