The paper describes the design method of a neural/fuzzy variable structural proportional-integral-derivative (neural/fuzzy VSPID) control system. The neural/fuzzy VSPID controller has a structure similar to that of the conventional PID. In this controller, the PD mode is used in the case of large errors to speed up response, whereas the PI mode is applied for small error conditions to eliminate the steady-state offset. A sigmoidal-like neuron is employed as a preassigned algorithm of the law of structural change. Meanwhile, the controller parameters would be changed according to local conditions. Bounded neural networks or bounded fuzzy logic systems are used for constructing the nonlinear relationship between the PID controller parameters and local operating control conditions. Flexible changes of controller modes and resilient controller parameters of the neural/fuzzy VSPID during the transient could thereby solve the typical conflict in nature between steady-state error and dynamic responsiveness. A neutralisation process is used to demonstrate the applicability of such a controller for controlling highly nonlinear processes.