This paper presents a neural network controller for reducing ship's roll motion using active-fin stabilizers. Classical PID controllers fail to control the ship's roll when dynamic parameters of ship change. An innovative approach was used to overcome this disadvantage wherein three different conditions of ship stability were chosen and PID controller was tuned separately for all the 3 cases. Then, training data is derived from these PID controllers to design a neural network controller. The NN controller is thereafter fine tuned by considering various input combinations while training with different number of hidden neurons so as to design the best neural network controller for reducing the ship's roll. The NN controller is also intended to avoid ship sinking in worst stability conditions.