In this paper, a dynamic neural PID control scheme is presented based on the Ziegler-Nichols-like formula. The neural PID with a single estimate is tuned according to the Lyapunov type stability criterion. The proposed control scheme can guarantee convergence of the estimate in neuron and the stability of the closed-loop system. Procedures are provided for selecting the most appropriate tuning method for a given application based on the primary function of the feedback loop (servo against regulatory) as well as the relative importance of control effort and robustness. In addition, the neural PID version of the Ziegler-Nichols controller, a popular strategy for systems with delays, was found to yield excellent setpoint following but sluggish rejection of unmeasured disturbances acting at the plant input. Simulation examples and comparisons using Ziegler-Nichols PID controllers and that of the fuzzy selftuning of PID control scheme for system with time delays are given to illustrate the performance of the proposed control scheme.