A wind turbine (WT) site optimization procedure was developed and applied on two different onshore and one offshore sites, supplied with tall met masts and belonging to exemplary wind climates. In addition to detecting the most suitable WT for each site, Kohonen's self-organizing maps (SOMs) were employed to improve investigation of those parameters mostly influencing site optimization. Three years (2013-2015) of 1-h vertical observations from local met masts and a database of 377 onshore and 23 offshore commercial WTs were used. As a result, maximizing capacity factor (CF) was confirmed as a good objective function, though not the best, which was minimizing levelized cost of energy (LCoE). In general, these two conditions do not necessarily match: variously setting WT parameters may either result in an LCoE reduction or CF increase, but both conditions do not occur concurrently. A key finding was that minimum LCoE cannot be achieved by indefinitely increasing the WT hub height, but rather through detection of an optimum value obtained as a unique solution of the optimization procedure. Furthermore, the capability of SOM to recognise the cluster structure of all parameters influencing WT site optimization shed further light on their mutual relationship, thus proving to be an ideal tool to address the non-convex nature of this issue. (C) 2017 Elsevier Ltd. All rights reserved.