In open world applications a number of machine-learning techniques may potentially apply to a given learning situation. The research presented here illustrates the complexity involved in automatically choosing an appropriate technique in a multistrategy learning system. It also constitutes a step toward a general computational solution to the learning-strategy selection problem. The approach is to treat learning-strategy selection as a separate planning problem with its own set of goals, as is the case with ordinary problem-solvers. Therefore, the management and pursuit of these learning goals becomes a central issue in learning, similar to the goal-management problems associated with traditional planning systems. This paper explores some issues, problems, and possible solutions in such a framework. Examples are presented from a multistrategy learning system called Meta-AQUA.