Environmental data sets are characterized by a huge amount of heterogeneous data from external fields. As the number of measured points grows, a strategy is needed to select and efficiently analyze the useful information from the whole data set. One efficient way of obtaining the validation-compression of data sets is the adoption of a restricted set of features that describe, with an assigned accuracy a subset of the whole data set. One characteristic feature of the environmental data is time dependency: in the medium and long term they are not stationary data sets. The aim of this work is to propose a feature extraction technique based on a new model of an unsupervised neural network suitable to analyze this kind of data. The paper reports the results obtained utilizing the above extraction and analysis procedure on a real data set on chemical pollutants. It is shown that the proposed neural network is able to identify correctly human and/or meteorological effects in the environmental data set.