The aim of the present work is to evaluate the output of several Wave Energy Converters (or WECs), by identifying the relevant wave parameters that influence their performances. Three offshore sites from Ireland, Spain and Portugal were considered for investigation. They were selected based on their capability to support the development of marine renewable systems, this being the case of the GEROA project (Green Energy Research for Offshore Atlantic). Firstly, the wave data coming from ERA5 (interval 2023-2025, hourly values) were statistically processed in order to establish some relevant patterns. Secondly, the performances of five WECs wereevaluated in terms of their power output, capacity factor and capture width. These WECs were selected in order to cover a wider range of operating principles (point absorber–attenuator–terminator), but also to include a wider range of power capacity that goes from 15 kW to 5.9 MW. Based on these results, a feed-forward neural network was assembled in order to establish the importance of the input variables (11 inputs – wave parameters) to the performances of the considered WECs (3 outputs). Based on the literature review, among the input variables, the average and 10th percentile of the significant wave height, wave period and wave power were included, while for the wave direction, only the average value was used. The importance of the input parameters significantly varies according to the selected geographical area and WEC system, but per total, the average wave height (Hs) and wave power (Pw) seem to be the most relevant ones.