This paper applies the partial least squares (PLS) method to the multiple-input multiple-output (MIMO) semiconductor processes in the run-to-run (R2R) control practice. Due to the property of batch processing, the semiconductor manufacturing processes frequently exhibit high multicollinearity among input variables and dependency among output variables. These two effects will typically cause variance inflation of the regression coefficient estimates which are utilized in triggering or updating the R2R controller. Furthermore, the process nonlinearity is also likely to occur in some semiconductor processes. As the nonlinearity exists, the performance of the exponentially weighted moving average (EWMA) controller is not adequate and becomes aggravated after a few transient runs. The PLS method is, essentially, well suited for situations where multicollinearity is present among input variables. To rectify the aforementioned difficulties that might realistically take place in practice, the PLS method is considered in this paper a potential estimation alternative to the standard regression method. Three types of R2R simulation studies are conducted to verify the advantages of the PLS method. The simulation results show that using the PLS method as the model-building technique helps the EWMA controller to yield more consistent and robust control outputs than purely using the conventional EWMA controller. (C) 2010 Elsevier Ltd. All rights reserved.