This research presents an anomaly detection approach based on autoencoders for wind turbine condition monitoring. The overall goal is to develop a methodology for assessing wind turbine health in order to enable preventative maintenance programs. First, SCADA signals are used as data input in the approach. The approach then examines the differences between the estimated values by the autoencoder models and the measured signals from the SCADA system. Next, the Kernel Density Estimation is used to determine the distribution of the expected output's error. Finally, to efficiently extract anomalous activity in the data, a novel dynamic thresholding approach is used. The outcome reveals that the method is capable of detecting potential abnormalities prior to the onset of a breakdown. It also verifies that the approach can alert operators to potential changes within wind turbines even when the alarm records show no alerts.