In this paper, a Random Forest based run-to-run controller is developed to meet the challenges of a high-mix production. The model that relates the optimal recipe parameter with various industrial features is trained off-line from historical data. The predicted optimal recipe parameter is used on-line to tune the process condition. Numerical experiments conducted on a Chemical Mechanical Planarization process are presented to illustrate the performance of the controller. Compared with the system in production, the proposed approach demonstrates higher flexibility and efficiency.