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The dynamics of pH process is highly nonlinear, time-varying with change in gain of several orders. It is very difficult to investigate the dynamic behavior of such systems using conventional modeling techniques. An effective approach is to partition the available data into subsets and approximate each subset by a simple piecewise linear model. Fuzzy clustering can be used as tool to partition the data where transitions between the subsets are gradual. In this paper, Takagi-Sugeno (T-S) model is developed for a nonlinear function and a pH process using fuzzy c-means and Gustafson-Kessel (G-K) clustering techniques. The result shows that G-K algorithm gives satisfactory results compared to c-means algorithm. The performance of the proposed model based on G-K algorithm is also compared with the results obtained by NARX and conventional fuzzy modeling techniques. The comparison shows the superiority of the proposed model.