Process control is a critical factor in semiconductor manufacturing. To predict IC characteristics, the present industry still relies heavily on human expertise. Manual selection for explanatory variables cannot deal with multi-variables effectively and thus often results in poor prediction accuracy. In this thesis, we adopt multivariate linear regression to predict RF IC characteristics. The wafer fabrication data provided by IC manufacturing company are taken as input variables, and the final function test data are target outputs. By adopting variable selection scheme, we are able to identify key input variables so as to enhance the model accuracy without losing robustness. Our study showed that the proposed approach can achieve 92.5% accuracy for the validation data. The yield rate was improved significantly.