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Web Service discovery and selection deal with the retrieval of the most suitable Web Service, given a required functionality. Addressing an effective solution remains difficult when only functional descriptions of services are available. In this paper, we propose a solution by applying Case-based Reasoning, in which the resemblance between a pair of cases is quantified through a similarity function. We show the feasibility of applying Case-based Reasoning for Web Service discovery and selection, by introducing a novel case representation, learning heuristics and three different similarity functions. We also experimentally validate our proposal with a dataset of 62 real-life Web Services, achieving competitive values in terms of well-known Information Retrieval metrics.