Optimal alignment of wind turbines to the wind direction is a crucial condition for the quality of power output and for the health of the turbines. Actually, bad alignment can cause degraded performances and dangerous loads that can affect, on the long run, the mechanical safety of the wind turbine. Supervisory Control And Data Acquisition (SCADA) systems are becoming widespread in modern wind energy technology because of the appreciable costs – benefits ratio. The common time scale of SCADA, yet, usually is not effective for misalignment diagnosis because the wind varies too rapidly. For this reason, misalignment is often diagnosed using ad hoc techniques as LIDAR-based or spinner anemometers. In the present work, it is shown that very useful indications for the diagnosis of misalignment can be obtained also from the SCADA data, without invoking expensive supplementary control techniques. The method is validated on the data set of a wind farm sited in Italy.