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Situation assessment tasks, e.g., medical diagnosis, battlefield reading, corporation status assessment for merger or acquisition purposes, are formulated as a general family of problem solving tasks. The generic nature of this family task as a multimembership hierarchical pattern recognition problem is characterized and the types of decision support systems (DSS) are identified. The focus is on knowledge representation and elicitation, although issues related to inference mechanisms, system structure, and expert-machine-user interface are also discussed. Two types of knowledge are distinguished: global knowledge and local knowledge. Global knowledge is required to determine directions on which to focus attention, while local knowledge is required for assessing the validity of a specific alternative based on a given set of findings. Global knowledge is represented as a network of relevancy pointers between alternatives and features. Attached to the links of this network are weights by which the strength of relevancy is evaluated and global directions (hypotheses) for situation analysis are determined. For local knowledge, it seems that in most practical problems multiple representation techniques would be required to characterize adequately the alternatives by means of their relevant features. The presentation is accompanied by examples for military situation assessment. However, comparable examples from medical and business applications are also cited. In fact, many of the ideas presented here have already been implemented in the MEDAS systemÂ¿a medical DSS for emergency and critical care medicine.