Since the exponential increase in installed wind power capacity worldwide in the last decade, there has been a growing concern for wind farm operation and maintenance strategies. Good O&M management reduces machine downtime and, consequently, production losses. The commitment to predictive maintenance strategies has generated very satisfactory results, since it is possible to monitor system behavior and act in advance of any problem. Predictive maintenance is based on the installation of several sensors. These sensors generate hundreds of data points every minute, which must be stored, processed and analyzed to become information, and the quality of these data determines the accuracy of the information. Therefore, it is necessary to develop methodologies that facilitate the quality treatment of the data originated by the sensors. This article presents a methodology to evaluate the quality of SCADA data from wind turbines. With this tool it is possible to obtain reliable data for decision making in the operation and maintenance of wind farms.