Abstract: A neural network based controller of agile missile capable of high angle of attack flight is presented. Super agility is needed in close-in combat. For the missile to possess super agility (high angle of attack capability) some form of alternate control is needed, vectoring the thrust (or using reaction jets) can provide this capability. With super agility requirements, missiles expected to maneuver in extreme flight conditions where there is a considerable amount of nonlinearity. The objective of this paper is to examine the use of neural network (NN) as an integral part of nonlinear dynamic inverse control. A fuzzy learning gain is adopted to enhance the stability of neural network. The NN are used to generate feedback control to force the plant toward desirable responses. A reference model is used to represent the ordered index of the closed-loop system performance. A benefit in the case is that the control system tends to be more robust. A simulation is presented and the results show the perfect tracking performance.