The acquisition of data from satellites is of great interest for the importance that the recognition of these data has in different application environments such as geology, hydrology, town planning, observation of agricultural sites or forests, and others. This paper faces the problem of the statistical classification of SAR images. To this end in the literature different methods have been proposed such as the K-NN or the maximum likelihood. Their use allows to achieve fast classification maps but the accuracy obtained is often not satisfactory enough. The reason is that those methods do not fully exploit the spatial correlation information because they use classical features that do not capture this property. Moreover the classical approaches make use of a fixed set of features which do not allow optimal classification. This fact is even more evident for SAR images, where classes are overlapped. In this paper, the use of classical features as the sample mean and the sample variance, which exploit the spatial correlation property, will be shown within a statistical image classification framework. The parametric feature estimators, together with a brief description of the developed classification algorithm, are presented. Throughout the paper the usual hypothesis of independent samples is not applied, due to the strong texture characteristics of SAR images. Finally a section containing results of classification tests followed by a short discussion can be found.