Due to the high costs for operation and maintenance in the wind power industry, many utility companies and wind farm owners are focusing on cost reduction for operation and maintenance. One of the approaches to reduce these costs is the application of condition monitoring systems and fault detection systems (FDS).The implementation of these two systems into the wind turbine will not only give the operator of the wind turbine the ability to predict the incipient faults at an early stage, but also enhance operational availability and safety of the wind turbine by avoiding the risk of unforeseen breakdowns. In this reported case study, a data mining technique using artificial neural networks was utilized to identify incipient faults and control the condition of gearbox in real time through the analysis of Supervisory Control and Data Acquisition (SCADA) data. Basically, the idea of this technique is that faults can be detected by comparing between normal behavior and the real behavior of the gearbox. Any deviation from the normal behavior will be flagged as an anomaly. The result presented demonstrates that although artificial intelligent network can be used for early fault identification for the gearbox, but it may be limited to the specific type of turbine modeled.