The uncertainty of ship models and interferences can lead to reduce the effectiveness of conventional roll stabilization controller depending upon accurate mathematical model.This paper proposes the design of neuro-fuzzy controller for rudder roll stabilization. Learning ability of neural network is utilized to optimize the fuzzy controller. Hybrid learning rule which is a combination of backpropagation algorithm and least square estimator is utilized to realized parameter adjustment of fuzzy control rule and membership function in order to improve the adaptive capacity of controller. The simulation tests under various situations such as different navigational speed, sea conditions, wave-to-course angle and ship parameter perturbation are to check the performance of roll stabilizatin controller. Simulation results show that the neuro-fuzzy controller has better robustness and effectiveness for rudder roll stabilization in beam seas.