Detecting anomalies is an important problem that has been widely researched within diverse research areas and application domains. The early detection of faults may help avoid product deterioration, major damage to the machinery itself and damage to human health. This study proposes a robust fault detection method with an Artificial Neural Network-Multi-Layer Perceptron (ANN-MLP) and a statistical module based on Wald's sequential probability ratio test (SPRT). To detect a fault, this method uses the mean and the standard deviation of the residual noise obtained from applying a NARX (Nonlinear Auto-Regressive with exogenous input) model. To develop the neural network model, the required training and testing data were generated at different operating conditions. To show the effectiveness of the proposed fault detection method, it was tested on a realistic fault of a distillation plant at the laboratory scale. (C) 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.