A technique based on information theory principles for the assessment of the degree of dissimilarity between binary images is proposed. The obtained results are then used to compute the optimal resolution needed for distinction between two images. We first describe how the image overall information content can be defined, identified, assessed, and analyzed. This concept is then extended to the measurement of the overall information content of more than one image. This information measure is then shown to be related to recognition information, which is a measure of the degree of dissimilarity between images. Recognition information provides a reliable way of evaluating the optimal scan resolution required for unambiguous recognition of an image. To demonstrate the feasibility of this technique, a series of tests are carried out on typed characters. The results illustrate the power of information assessment techniques in analyzing the recognition capabilities of a pattern recognition system.