In the context of Operation and Maintenance of wind energy infrastructure, it is important to develop decision support tools, able to guide engineers in the management of these assets. This task is particularly challenging given the multiplicity of uncertainties involved, from the point of view of the aggregated data, the available knowledge with respect to the wind turbine structures, as well as the varying operational and environmental loads. We propose to propagate wind turbine telemetry through a decision tree learning algorithm to detect faults, damage, and abnormal operations. The use of decision trees is motivated by the fact that they tend to be easier to implement and interpret than other quantitative data-driven methods. Furthermore, the telemetry consists of data from condition and structural health monitoring systems, which lends itself nicely in the field of Big Data as large amounts are continuously sampled at high rate from thousands of wind turbines. In this paper, we review several decision tree algorithms, we then train an ensemble Bagged decision tree classifier on a dataset from an offshore wind farm comprising 48 wind turbines, and use it to automatically extract paths linking excessive vibrations faults to their possible root causes. We finally give an outlook of a cloud computing based architecture to implement decision tree learning involving Apache Hadoop and Spark.