The need to obtain automatic filters to treat wind data is clear due to the huge amount of data available in any wind farm analysis. Automatic detection of performance deviations of wind turbines is required for maintenance purposes. In spite of some alarms being registered by the wind farm's SCADA system, many times automatic use of real data use is not valid due to its low quality. In this paper, the problems to carry out the characterization of the turbines in a wind farm are stated and a new technique to filter the data is presented. The robust statistical technique of the Least Median of Squares (LMedS) combined with a random search is adapted to deal with this problem with promising results. A mathematical model representing the dependency of the wind turbine behaviour in function not only of the wind speed but also of other variables such as the direction of the wind is also proposed. The results achieved are tested with long term real data from wind farms in Spain. (C) 2009 Elsevier Ltd. All rights reserved.