According to the records of Hong Kong rail operator, MTR Corporation, the weekly electrical consumption of each railway station ranges from 18 MWh to 230 MWh. Since the electrical consumption of stations is a major factor in the planning of infrastructure, a good prediction of the electrical consumption will greatly assist in the design of the station infrastructure. This study develops an intelligent approach to predict the energy consumption of railway stations. Multi-layered Perceptron (MLP) is adopted to mimic the non-linear correlation between energy consumption, the spatial design of the station, meteorological factors and also the usage of the 19 stations selected. Coefficient of correlation is obtained between the MLP predicted results and the actual collected data to evaluate the performance of the prediction. We apply statistical approach to assess the performance of the developed MLP model. It shows that minimum coefficient of correlation is 0.96 with a 95% confidence level which is considered sufficient for engineering application. This approach is also adopted to predict the profile of the weekly electrical consumption of a selected station. The predicted profile reasonably agrees with that of the actual consumption. This study develops a useful tool to estimate the electrical power consumption of new MTR stations. (C) 2012 Elsevier Ltd. All rights reserved.