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Based on the bounded property and statistics of chaotic signal and the idea of set-membership identification,we propose a set-membership generalized least mean square (SM-GLMS) algorithm with variable step size for blind adaptive channel equalization in chaotic communication systems. The steady state performance of the proposed SM-GLMS algorithm is analysed, and comparison with an extended Kalman filter (EKF)-based adaptive algorithm and variable gain least mean square (VG-LMS) algorithm is performed for blind adaptive channel equalization. Simulations show that the proposed SM-GLMS algorithm can provide more significant steady state performance improvement than the EKF-based adaptive algorithm and VG-LMS algorithm.