Reliability of wind turbine components and maintenance optimisation are among the critical aspects of wind power development closely related to profitability and future development. The main reason for research in these areas is lowering the cost of energy production for wind power, specifically important in offshore environment. Continuous monitoring of specific wind turbine components can be valuable for wind farm operators and, subsequently, wind farm owners.  Also, health assessment of critical components can be useful in estimating the possibilities for life extension of wind turbines. Expensive Condition Monitoring Systems (CMSs) are not always available, particularly in older wind farms, and additionally installing CMSs on wind turbines is not always economically feasible. However, most of modern wind turbines are equipped with the Supervisory Control And Data Acquisition (SCADA) system which is recording 10-minute average values of parameters that depict operation of the turbine. That being said, SCADA data contains a vast amount of information that can be used for analysis of wind turbine components health. Therefore, this project will present an application of previously published methodology for SCADA data condition monitoring on real wind farm data. The goal of this project is to investigate on the possibilities of the SCADA monitoring methodology and what can be the added value of the application for wind farm operators, owners and other stakeholders. The methodology for condition monitoring through SCADA data was applied on real data gathered from two wind farms in Germany and one in the Netherlands. During the project the methodology had to be modified in order to ensure the best possible industrial application. Results of the project showed that the SCADA data condition monitoring approach is not capable of predicting failures. However, the technique has been proven successful for detecting the changes of trends in dependencies of working parameters, specifically monitoring parameters related to the turbine generators. Continuously monitoring the dependencies of working parameters can be used as an additional source of information for maintenance scheduling and assessment of components health. The approach presented in this paper can be valuable to asset managers and wind farm owners.