Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.