This study investigates the issue of adaptive fault-tolerant neural control in strict-feedback nonlinear systems. The system is subjected to actuator faults, dead-zone and saturation. To model the unknown functions, radial basis function neural networks (RBFNN) are employed. The proposed approach utilizes a backstepping technique to formulate an adaptive fault-tolerant controller, drawing upon the Lyapunov stability theory and the approximation capabilities of RBFNN. The resultant controller guarantees the boundedness of all signals in the closed-loop system, ensuring precise tracking of the reference signal by the system output with a small, bounded error. Finally, simulation results are provided to illustrate the efficacy of the proposed strategy in addressing actuator faults, dead-zone, and saturation.