In this paper, an on-line learning neuro-control scheme that incorporates a growing radial basis function network (GRBFN) is proposed for a nonlinear aircraft controller design. The scheme iu based on Feedback-error-learning strategy in which the neuroflight-controller (NFC) augments a conventional controller in the loop. Bq using the: Lyapunov synthesis approach, the tuning rule for updating all the parameters of the RBFN weights. widths and centers of the Gaussian functions) is derived which ensures the stability of the overall system with improved tracking accuracy. The theoretical results are validated using simulation studies based on a nonlinear 6-DOF high performance fighter aircraft undergoing a high alpha stability-axis roll maneuver. Compared with a traditional RBFN where only the weights are tuned, a GRBFN with tuning of all the parameters can implement a more compact network structure with smaller tracking error.