Considerable planning and learning research has been devoted to the problem of automatically acquiring search control knowledge to improve planning efficiency. However, most speed up learning systems define planning success rather narrowly, namely as the production of any plan that satisfies the goals regardless of the quality of the plan. As planning systems are applied to real world problems, concern for plan quality becomes crucial. Many researchers have pointed out that generating good quality plans is essential if planning systems are to be applied to practical problems (Wilkins 1988; Drabble, Gil, and Tate 1995; Perez 1996; Ruby ~ Kibler 1993). The problem is that in most practical situations we only have the post-facto 1 knowledge about plan quality. Such knowledge allows us to determine the quality of a plan once complete plans have been generated but it is of little use during plan construction. The learning problem then is that of translating the postfacto quality measurement knowledge into operational knowledge that can be used during plan generation to guide the planner towards making choices that lead to better plans. To address this issue, we outline a technique of learning to improve the quality of plans generated by partial-order planners. This technique can be integrated with speed-up learning methods. We call resulting approach a Performance Improving Partialorder Planner (PIPP). This paper presents the key ideas underlying this approach.