The concept of a qualitative model provides a unifying perspective for understanding how expert systems differ from conventional programs. Knowledge bases contain qualitative models of systems in the world, that is, primarily non-numeric descriptions that provide a basis for explaining and predicting behavior and formulating action plans. The prevalent view that a quantitative model must be a simulation, to the exclusion of prototypic and behavioral descriptions, has fragmented our field, so that we have failed to usefully synthesize what we have learned about modeling processes. For example, our ideas about "scoring functions" and "casual network traversal," developed apart from a modeling perspective, have obscured the inherent explanatory nature of diagnosis. While knowledge engineering has greatly benefited from the study of human experts as a means of informing model construction, overemphasis on modeling the expert's knowledge had detracted from the primary objective of modeling a system in the world. Placing artificial intelligence squarely in the evolutionary line of teleologic and topologic modeling, this paper argues that the study of network representations has established a foundation for a science and engineering of qualitative models. Four figures and 84 references are provided. (Author/EW)