A great amount of parameters can be derived from the original bands of multispectral remotely-sensed irnages. In particular, for classification purposes it is important to select which of these parameters allow the classes of interest to be well separated in the feature space. In fact, both classification accuracy and computational efficiency rely on the set of features used. Unfoltunately, as spectral responses are strongly influenced by various environmental factors (e.g., atmosphere interferences and non- homogeneous sunshine distribution) the derived parameters depend not only on the considered classes but also on the peculiar characteristics of analyzed images. Even if many studies have been carried out both to identify more stable parameters and to correct images, the problem is still open. It cannot be a-priori solved on the basis of the only ground classes considered, but an ad-hoc selection is required for each image to be classified. In literature, several feature-selection criteria have been proposed. In this paper, a critical review of different techniques to accomplish feature-selection for remote-sensing classification problems is presented. To preserve the physical meaning of selected features only criteria that do not make transformation of the feature space are considered. Most of such criteria were originally defined to evaluate the separability among couple of classes. A formal extension of these techniques based on the statistical theory to face also multiclass cases is considered and compared with traditional heuristic extensions. Finally, with the aim to give a good approximation of the Bayes error probability a new feature-selection criteria is proposed. Preliminary tests carried out on a multispectral data-set witness its potentialities.