Abstract One of the first steps for the exploitation of any energy source is necessarily represented by its estimation and mapping at the aim of identifying the most suitable areas in terms of energy potential. In the field of renewable energies this is often a very difficult task, because the energy source is in this case characterized by relevant variations over space and time. This implies that any temporal, but also spatial, estimation model has to be able to incorporate this spatial and temporal variability. The paper deals with the spatial estimation of the wind fields in Sicily (Italy) by following a data-driven approach. Starting from the results of a preliminary study, a novel technique resulting from the integration of neural and geostatistical techniques was developed in order to obtain the wind speed maps for the region at 10 and 50 meters above the ground level. The mean values of the theoretical Weibull distribution function describing the wind regime at each of the available measurement sites were used to train a multi-layer perceptron (MLP) whose goal is to compute the most of the wind spatial trends. Other pieces of information about the territory (altitude, land coverage) were also used as inputs of the network and organized into a geographic information system (GIS) environment. The remaining de-trended linear means have been computed by using a universal kriging (UK) estimator. The results of these steps were then summed up and it was thus possible to obtain a map of the estimated wind fields.