Condition monitoring in wind turbines aims at detecting incipient faults at an early stage to improve maintenance. Artificial neural networks are a tool from machine learning that is frequently used for this purpose. Deep Learning is a machine learning paradigm based on deep neural networks that has shown great success at various applications over recent years. In this paper, we review unsupervised and supervised applications of artificial neural networks and in particular of Deep Learning to condition monitoring in wind turbines. We find that – despite a promising performance of supervised methods – unsupervised approaches are prevalent in the literature. To explain this phenomenon, we discuss a range of issues related to obtaining labelled data sets for supervised training, namely quality and access as well as labelling and class imbalance of operational data. Furthermore, we find that the application of Deep Learning to SCADA data is impeded by their relatively low dimensionality, and we suggest ways of working with higher-dimensional SCADA data.