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Unplanned production shutdown due to equipment failure is the source of the highest cost in the manufacturing and process industries. Traditional fault detection methods are able to monitor the process and detect deterioration of the equipment after their degradation and malfunction occurs. This paper presents an intelligent technique based on a neural network (NN) that monitors the health of the equipment and forecasts faults by detecting any onset of failures. In this approach, an adaptive modular NN architecture that is capable of monitoring the health of industrial machines is introduced. This technique is applied to a subsystem of a machining center. The high accuracy of the technique is verified by extensive tests, resulting in over 99% precision.