In maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a proportional integral derivative controller (PID) is proposed in order to improve the accuracy of predictions. The PID controller attempts to correct the error between the real process variable and the neural network predictions. The approach and its performances are illustrated by using two classical prediction benchmarks: the Mackey–Glass chaotic time series and the Box–Jenkins furnace data.