A gain adaptive sliding mode controller based on interval type-II fuzzy neural network identification was proposed to handle the system uncertainty and the external disturbances come from the attitude angle disturbance of a Micro Aircraft Vehicle (MAV). Firstly, the attitude dynamical model of MAV was established. Then, the interval type-II fuzzy neural network was used to approximate the nonlinearity function and uncertainty functions in the attitude angle dynamic model of the MAV. The correct items from the gain adaptive sliding mode controller were taken to compensate identification errors and load disturbances. Finally, Lyapunov stability theorem was designed and the adaptive law and the sliding mode gain adaptive law for adjusting on line interval type-II fuzzy neural network parameters were obtained under the condition of asymptotic stability of the closed-loop system. The numerical simulation and comparison were performed and the results show that the proposed control system has not only stronger robustness to system uncertainty and external disturbances but also more excellent steady characteristics and tracking accuracy as compared with the conventional adaptive sliding model controller and the type-I fuzzy neural network based sliding mode controller.