Bearings are an essential part of turbine generators and gearboxes. Dynamic and unpredictable stress causes the bearings to wear prematurely, leading to increased turbine maintenance costs, and could lead to sudden, expensive turbine breakdowns. Over temperature impacts the performance of turbine bearings. In this paper, data mining is applied to identify bearing faults in wind turbines. Historical wind turbine data are analyzed to develop prediction models for bearing faults. Such models are generated by neural network algorithms, using data from 24 turbines collected over a period of four months. Models predicting normal behavior are constructed. The performance of the models is validated on different wind turbines with over 97% accuracy. The model error residuals are analyzed using moving average windows to predict the occurrence of over-temperature events. Five over-temperature events are analyzed. The research reported in this paper has led to the prediction of faults 1.5 h before their occurrence. (C) 2012 Elsevier Ltd. All rights reserved.