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Control of a pH process is great difficulty due to time varying and nonlinear characteristics. Fuzzy logic has been successfully applied to many applications in control with uncertainties. An important consideration in designing any fuzzy control system is the formation of the fuzzy rules and the membership functions. Generally the rules and the membership functions are formed from the experience of the human experts. With an increasing number of variables, the possible number of rules for the system increases exponentially, which makes it difficult for experts to define a complete rule set for good system performance. Also the system performance can be improved by tuning the membership functions. In a fuzzy system the membership functions and rule set are codependent, they are encoded into the chromosome and evolved simultaneously using genetic algorithm. The performance of the proposed approach is demonstrated through development of fuzzy controller for a benchmark pH process. In both set point tracking and disturbance rejection, simulation results show the better performance when compared to fuzzy logic controller.