Tool builders have focused, not improperly, on tool building--how to build better performing machine problem-solvers, where the implicit model is a human expert solving a problem in isolation. A critical task then for the designer working in this paradigm is to collect human knowledge for computerization in the stand alone machine problem-solver. But tool use involves more. Building systems that are problem-solvers in isolation does not guarantee high performance in actual work contexts where the performance of the joint person-machine system is the relevant criterion. The key to the effective application of computational technology is to conceive, model, design, and evaluate the joint human-machine cognitive system (Hollnagel & Woods, 1983). Like Gestalt principles in perception, a decision system is not merely the sum of its parts, human and machine. The configuration or organization of the human and machine components is a critical determinant of the performance of the system as a whole (e.g. Sorkin & Woods, 1985). The joint cognitive system paradigm (Woods, 1986; Woods, Roth & Bennett, in press) demands a problemdriven, rather than technology-driven, approach where the requirements and bottlenecks in cognitive task performance drive the development of tools to support the human problem-solver. In this paper we describe an approach to understand the cognitive activities performed by joint human-machine cognitive systems. The real impediment to effective knowledge acquisition is the lack of an adequate language to describe cognitive activities in particular domains--what are the cognitive implications of some application's task demands and of the aids and interfaces available to the people in the system; how do people behave/perform in the cognitive situations defined by these demands and tools. Because this independent cognitive description has been missing, an uneasy mixture of other types of description of a complex situation has been substituted---descriptions in terms of the application itself, of the implementation technology of the interfaces/aids, of the user's physical activities or user psychohaetrics. We describe one approach to provide an independent cognitive description of complex situations that can be used to understand the sources of both good and poor performance, i.e. the cognitive problems to be solved or challenges to be met.