Feature-based automatic target recognition (ATR) discriminates between target classes on the basis of the values taken by certain target features. The conventional approach is to select the best features for a particular task from a large set of features which have been pre-defined on the basis of physical intuition. A simple feature might be target area whilst a more sophisticated feature might be some measure of fractal dimension. ATR performance will be influenced by the choice of features and by the accuracy with which the statistical behaviour of these features has been characterised. This paper describes a technique which can be used to determine statistical feature behaviour despite limited examples of target realisations. It also addresses the problem of feature choice by introducing a method for assessing the information carried by different features. This leads to a potential technique for adaptive feature generation. These ideas are illustrated by application to synthetic aperture radar (SAR) images of vehicles.