Wind power has been an important part of the energy market for the last decade. Anyway, the main problems associated with wind farms are operation and maintenance costs, which represent up to one-third of the total cost of energy production. The purpose of this thesis is to analyze the behavior of the turbines located on a wind farm in southern Italy to detect in real-time any malfunctions or alarms generated by the control system. Several Feedforward Neural Networks (FNN) and Recurrent Neural Networks (RNN) models have been created starting from data coming from Supervisory Control And Data Acquisition systems (SCADA). This made it possible to build both the characteristic behavior of the critical components of wind turbines and a Statistical Process Control (SPC), useful to evaluate their anomalous behavior.