A ball mill grinding circuit is a nonlinear system characterised by significant controller interaction between the manipulated variables. A rigorous ball mill grinding circuit is simulated and used in its entirety for the development of a neurocontroller through the use of evolutionary reinforcement learning. Reinforcement learning entails learning to achieve a desired control objective from direct cause-effect interactions with a simulated process plant. The SANE (symbiotic adaptive neuro-evolution) algorithm is able to learn implicitly to eliminate controller interactions in the grinding circuit by, taking a plant wide approach to controller design. The ability of the neurocontroller to maintain high performance in the presence of large disturbances in feed particle size distribution and ore hardness variations is demonstrated. The generalisation afforded by the SANE algorithm in dealing with considerable uncertainty in its operating environment attests to a large degree of controller autonomy.