In the context of Computer Aided Process Planning (CAPP), feature recognition as well as the generation of manufacturing process plans are very diffi cult problems. The selection of the best manufacturing process plan usually involves not only measurable factors, but also idiosyncrasies, preferences and the know-how of both the company and the manufacturing engineer. In this scenario, mixed-initiative techniques such as plan recognition, where both human users and intelligent agents interact proactively, are useful tools for improving engineer’s productivity and quality of process plans. In order to be effective, these intelligent agents must learn autonomously this preferences and know-how. The problem of learning plan libraries for plan recognition has gained much importance in recent years, because of the dependence of the existing plan recognition techniques on them, and the diffi culty of the problem. Even when there is considerable work related to the plan recognition process itself, less work has been done on the generation of such plan libraries. In this paper, we present some preliminary ideas for a new approach for acquiring hierarchical plan libraries automatically, based only on a few simple assumptions and with little given knowledge.