Equipment wear is strongly related to the performance of grinding circuits. In particular, SAG mills are subjected to the wear of their shell liners and grates. Changes in lifter height and face angle impact mill performance, while classification at the mill discharge is also affected as grates wear out. Moreover, liner mass loss caused by wear affects how operators interpret readings of sensors such as load cells or bearings pressure. Automating and optimising grinding circuits under these constrain becomes challenging due to the nonlinear and dynamic nature of the system. The operation of SAG mill grinding circuits remains largely dependent on human intervention, which frequently results in inconsistencies in performance across shifts, reflecting the operator’s personal preferences, experience, and other human factors. The influence of human factors can be reduced by careful training of operators as well as by automation using advanced supervisory control. The latter approaches could benefit from the development of dynamic process models that consider the effects of wear on the performance of equipment over time. This paper presents detailed industrial data showing the effects of wear on SAG mill performance. Additionally, a dynamic SAG mill model that accounts for these phenomena is proposed and demonstrated in a simulated environment. This model is shown to mimic the effect of wear on the performance of industrial scale mills and the operational constraints they are subject to, thus extending traditional SAG mill models. Incorporating wear into modelling and simulation platforms can improve the understanding of the effects that wear can have on the performance of key assets in comminution circuits and may allow the development of more robust model-based advanced process control strategies.