Soar, an architecture for problem solving and learning based on heuristic search and chunking, has been applied to a variety of tasks during the development of the Soar project, the goal of which is to build a system capable of general intelligent behavior. The hypothesis being tested in this aspect of Soar research is that chunking, a simple experience-based learning mechanism, can form the basis for a general learning mechanism. Previous work has demonstrated how the combination of chunking and Soar could acquire search-control knowledge (strategy acquisition) and operator implementation rules in both search-based puzzle tasks and knowledge-based expert systems tasks. This paper provides a new demonstration of the capabilities of chunking in the context of the macro-operator technique and shows how: (1) this technique can be used in a general, learning problem solver without the addition of new mechanisms; (2) the learning can be incremental during problem solving rather than requiring a preprocessing phase; (3) the macros can be used for any goal stated in the problem; and (4) additional generality can be obtained via transfer of learning between macro-operators if an appropriate representation of the task is available. References and a distribution list are provided. (KM)