Wind power is currently the fastest growing exploited source of energy globally. Hence there is an urgent need to understand how wind turbines perform from different perspectives. Even though condition monitoring systems have a huge impact in optimizing wind farm performance via fault anticipation, they do omit several aspects concerning performance. Seemingly, there is a scarcity of studies that attempt to deliver a quick and practical method for wind farm performance analysis, which is the aim of this study. This paper presents a methodology for evaluating the performance of operating wind farms via the use of the Supervisory Control and Data Acquisition System (SCADA) and modeled data. The potential annual energy is calculated per individual turbine, factoring in underperforming/loss events to present their power output in accordance with a representative derived operational power curve. Losses/underperformance events are calculated and categorized into several groups, aimed at identifying and quantifying their causes. The methodology requires both anemometry data from the SCADA system, an onsite meteorological mast, a lidar in combination with the mast as well as modeled data. The discrepancy of the data representing the valid points of the power curve is also taken into consideration when assessing performance, i.e. wind speed vs power output of events that are not loss/underperformance. Production loss and relative standard deviation of power/energy output are the main results obtained in this paper. Finally, a number of optimization measures are suggested in order to boost performance, which can enhance a wind farm’s financial results. To assess the reliability of the proposed methodology, a case study was conducted and evaluated. The case study concerns a windfarm with nominal capacity of 21MW in Kitheronas, Viotia county, Greece which has been operational since November 2014. The case study shows that the methodology is capable of determining potential energy and associated losses/underperformance events. Several questions were raised during the assessment and are discussed in this work, recommendations for optimization measures are presented at the end of the paper. It also contains a discussion on the limitations and uncertainties associated with the presented methodology and case study.