It has been argued that knowledge-based systems (KBSs) must reason from evidential information, i.e., from information that is to some degree uncertain, imprecise, and occasionally inaccurate. This is no less true of KBSs that operate in the domain of computer-based image interpretation. Recent research has suggested that the work of Dempster and Shafer (DS) provides a viable alternative to Bayesian-based techniques for reasoning from evidential information. In this paper, we discuss some differences between the DS theory and some popular Bayesian-based approaches to effecting the reasoning task. We then discuss some work on integrating the DS theory into a knowledge-based high-level computer vision system in order to examine various aspects of this new technology that have not been explored to date. Results from a large number of image interpretation experiments are presented. These results suggest that a KBS's performance improves substantially when it exploits various features of the DS theory that are not readily available in pure Bayesian-based approaches.