Condition monitoring helps reduce the operations and maintenance costs by providing information about the physical condition of wind power systems. This study proposes to use a statistical method for effective condition monitoring. The turbine operation is significantly affected by external weather conditions. We model the wind turbine response as a function of weather variables, using a nonparametric regression method named least squares support vector regression. In practice, online condition monitoring of wind power systems often relies on datasets contaminated with outliers. This study proposes to use a weighted version of least squares support vector regression that provides a formal procedure for removing the outlier effects. We determine the decision boundaries to distinguish faulty conditions from normal conditions by examining the variations in the operational responses that are significantly affected by external weather. The results show that the proposed method effectively detects anomalies.