Summary: We propose a novel approach to fast fuzzy modeling based on a new incremental support vector regression (SVR). Firstly a candidate support vectors selection strategy based on kernel Mahalanobis distance measurement is proposed. This strategy is further used to develop a new incremental learning algorithm to speed up the training process of SVR. Then a hybrid kernel function is utilized to represent an SVR model as a TS fuzzy model. Finally a set of fuzzy rules can be directly extracted from the learning results of SVR. Experimental results of two benchmark examples show that the proposed model not only possesses satisfactory accuracy and generalization ability but also costs less computational time.