Abstract : This report develops concepts that will support the evaluation planning for the MSTAR features and feature extractors. These concepts will be used later in building a detailed evaluation plan. We began our development by distinguishing between the evaluation of a feature set and the evaluation of an extractor. The specifics for feature evaluation depend upon whether or not it is meaningful to define a truth-value; but in either case, features are evaluated in terms of their sensitivity (at first individually and then as a set) to various "factors". The factors of interest fall into the categories of Known, Class, and Noise. Ideal features would be discriminating (high sensitivity to class factors), robust (low sensitivity to noise factors), and predictable (predictable sensitivity to known factors). The evaluation of extractors (including auxiliary information such as runtime/memory use estimates and feature uncertainty) is based on accuracy (when meaningful), design quality, and good software engineering principles.