A control system based on a combination of two artificial neural networks (ANN's) was designed for the pH-neutralization of acidic liquid streams. The first ANN is a plant neural model that predicts future pH values from past/present values of pH and valve stem position and future values of valve stem position. The second ANN is a plant inverse neural model that calculates the future valve stem positions from present/past values of pH and valve stem position and future values of set point. The performance of the controller was studied first by numerical simulation. The controller was further implemented in a continuous stirred tank reactor in which the neutralization of acetic and propionic acids with sodium hydroxide was performed. The controller robustness and adaptive performance were tested under different perturbations of flow, composition, and set point and several buffering changes.