This paper presents a Hybrid Adaptive Neuro Fuzzy Control technique for speed control of BLDC motor drives. The proposed controller is an integration of adaptive neuro fuzzy, fuzzy PID and PD controllers. The objective is to utilize the best attribute of fuzzy PID and PD controllers, which exhibits a better response than the neuro fuzzy controllers. The error back propagation learning algorithm (EBPA) is used to train the data to minimize learning error. To validate the performance of proposed controller, simulations are done in MATLAB and comparison is made with PI, PD and fuzzy PID controllers. In addition, the performance of proposed controller is benchmarked with other controllers reported in the literature. The results of the proposed controller are promising in terms of quick settling time, zero peak overshoot and zero steady state error.