Two standard schemes for learning in classifier systems have been proposed in the literature: the bucket brigade algorithm (BBA) and the profit sharing plan (PSP). The BBA is a local learning scheme which requires less memory and lower peak computation than the PSP, whereas the PSP is a global learning scheme which typically achieves a clearly better performance than the BBA. This "requirement versus achievement" difference, known as the locality/globality dilemma, is addressed in this paper. A new algorithm called hierarchical chunking algorithm (HCA) is presented which aims at synthesizing the local and the global learning schemes. This algorithm offers a solution to the locality/globality dilemma for the important class of reactive classifier systems. The contents is as follows. Section 1 describes the locality/globality dilemma and motivates the necessity of its solution. Section 2 briefly introduces basic aspects of (reactive) classifier systems that are relevant to this paper. Section 3 presents the HCA. Section 4 gives an experimental comparison of the HCA, the BBA and the PSP. Section 5 concludes the paper with a discussion and an outlook on future work.