In this paper, we present the basic components of the prototype of an expert system that is capable of recognizing major brain structures given a set of integrated brain images. The proposed medical image understanding system, which is based on the blackboard architecture, employs the Dempster-Shafer (D-S) model as its inference engine to mimic the reasoning process of a human expert in the task of dividing a set of spatially correlated x ray CT, proton density (PD), and T2-weighted MR images into semantically meaningful entities and identifying these entities as respective brain structures. Within the framework of D-S reasoning, belief interval is adopted to represent the strengths of evidence and the likelihoods of hypotheses. By using the complicated blackboard-based architecture and D-S model, the proposed system can perform the task of recognition efficiently. Several experimental results are given to illustrate the performance of the proposed system.