Various unsupervised algorithms for vector quantization can be found in the literature. Being based on different assumptions, they do not all yield exactly the same results on the same problem. To better understand these differences, this article presents an evaluation of some unsupervised neural networks, considered among the most useful for quantization, in the context of a real-world problem: radioelectric wave propagation. Radio wave propagation is highly dependent upon environmental characteristics (e.g. those of the city, country, mountains, etc.). Within the framework of a cell net planning its radiocommunication strategy, we are interested in determining a set of environmental classes, sufficiently homogeneous, to which a specific prediction model of radio electrical field can be applied. Of particular interest are techniques that allow improved analysis of results. Firstly, Mahalanobis' distance, taking data correlation into account, is used to make assignments. Secondly studies of class dispersion and homogeneity, using both a data structure mapping representation and statistical analysis, emphasize the importance of the global properties of each algorithm. In conclusion, we discuss the advantages and disadvantages of each method on real problems.