The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for similarity assessment than traditional similarity metrics. The learning of fuzzy similarity rules is performed by exploiting the case base, which is utilized as a valuable resource with hidden knowledge for similarity learning. A sample of similarity is created from a pair of known cases in which the vicinity of case solutions reveals the similarity of case problems. We do pair-wise comparisons of cases in the case base to derive adequate training examples for learning fuzzy similarity rules. The empirical studies have demonstrated that the proposed approach is capable of discovering fuzzy similarity knowledge from a rather low number of cases, giving rise to the competence of CBR systems to work on a small case library.