In this paper we describe BOLERO, a case-based reasoner that learns strategic knowledge (plans) to improve the problem-solving capabilities of an expert system. As a planner, BOLERO is a reactive planner that when gathering new observations can immediately generate a new plan to cope with the new situations. As a learner, BOLERO is capable of learning strategies from observation of the problem-solving performed by a teacher. From this experience, BOLERO plans strategies that solve new problems. BOLERO learns from success and failure during its problem-solving process. An evaluation to measure the efficiency of BOLERO is performed by comparing the system's results against both the correct solution of a case and the solutions provided by different domain experts. BOLERO has been proved useful in acquiring strategic knowledge and in refining existing strategies, in a real-life expert system for pneumonia diagnosis.