The crude preheat train (CPT) in a petroleum refinery consists of a set of large heat exchangers which recovers the waste heat from product streams to preheat the crude oil. In these exchangers the overall heat transfer coefficient reduces significantly during operation due to fouling. The rate of fouling is highly dependent on the properties of the crude blends being processed as well as the operating temperature and flow conditions. The objective of this paper is to develop a predictive model using statistical methods which can a priori predict the rate of the fouling and the decrease in heat transfer efficiency in a heat exchanger. A neural network based fouling model has been developed using historical plant operating data. Root mean square error (RMSE) of the predictions in tube- and shell-side outlet temperatures of 1.83 and 0.93, respectively, with a correlation coefficient, R2, of 0.98 and correct directional change (CDC) values of more than 92 show that the model is adequately accurate. A case study illustrates the methodology by which the predictive model can be used to develop a preventive maintenance scheduling tool. © 2007 Elsevier Ltd.