The limited success and acceptance of automated process planning methods in the industry can be traced to the fact that most existing approaches aim at complete automation. We believe that the quest for complete automation is flawed, both because in practice optimality metrics for process plans are context-sensitive, and because there is significant organizational resistance to approaches that completely eliminate humans from the process planning framework. In this paper, we present an interactive and iterative planning framework, called ASUPPA, which focuses instead on providing intelligent assistance to a human process planner. After generating a “good” default process plan, ASUPPA engages in a “present – elicit criticism – revise” loop with an expert process planner. To operate successfully, ASUPPA needs access to the full search space of process plans, and have the ability to incrementally modify plans in response to expert criticism. The former is provided by basing ASUPPA on ASU Features Testbed, a comprehensive and systematic framework for recognizing and reasoning with features in machinable parts. To support the latter, the system is equipped with an iterative and interactive search mechanism. We will discuss the operational details of the resultant system, called ASUPPA.