In this paper, a modified version of the Support Vector Machine (SVM) is proposed as an empirical model for polymerization processes modeling. Usually the exact principle models of polymerization processes are seldom known; therefore, the relations between input and output variables have to be estimated by using an empirical inference model. They can be used in process monitoring, optimization and quality control. The Support Vector Machine is a good tool for modeling polymerization process because it can handle highly nonlinear systems successfully. The proposed method is derived by modifying the risk function of the standard Support Vector Machine by using the concept of Locally Weighted Regression. Based on the smoothness concept, it can handle the correlations among many process variables and nonlinearities more effectively. Case studies show that the proposed method exhibits superior performance as compared with the standard SVR, which is itself superior to the traditional statistical learning machine in the case of high dimensional, sparse and nonlinear data.