Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A supervisory control and data acquisition (SCADA) system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT's health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purposes; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. This study proposes a new method for analysing WT SCADA data by using an a priori knowledge-based adaptive neuro-fuzzy inference system with the aim to achieve automated detection of significant pitch faults. The proposed approach has been applied to the pitch data of two different designs of 26 variable pitch, variable speed and 22 variable pitch, fixed speed WTs, with two different types of SCADA system, demonstrating the adaptability of the approach for application to a variety of techniques. Results are evaluated using confusion matrix analysis and a comparison study of the two tests is addressed to draw conclusions.