With the rapid growth of offshore wind energy capacity, the operations and maintenance (O&M) challenges have increased significantly. Prognostics is the ability to know the condition of an equipment and to plan and perform maintenance prior to critical failure. The generator of the wind turbine is one of the most expensive components and therefore accurate prognostics of the generator can reduce costs of O&M. The existing solutions for prognostics rely on expensive, purpose built condition monitoring systems. This research presents a prognostic method to predict the remaining useful life (RUL) of generators, that uses no additional hardware beyond the standard SCADA systems installed in turbines. By applying machine learning techniques to detect anomalies in the data, the health of the generator is quantified into an Anomaly Operation Index (AOI). The degradation of the generator is then studied using a time series analysis method to estimate the generator’s RUL. The experimental study on real-world wind turbine data shows that this method predicts the RUL of the generator with good accuracies and provides sufficient lead times to the operators to schedule maintenance and repair.