We apply relational programming techniques to the problem of synthesizing efficient implementation for pattern matching construct. Although in principle pattern matching can be implemented in a trivial way, the result suffers from inefficiency in terms of both performance and code size. Thus, in practice of functional languages implementation alternative, more elaborated, approaches are widely used. However, as there are multiple kinds and flavors of pattern matching construct, these approaches have to be specifically developed and justified for each concrete inhabitant of pattern matching “zoo”. We formulate pattern matching synthesis problem in relational terms and develop a few optimizations which improve the efficiency of the synthesis and guarantee the optimality of the result. Our approach is based on relational representation of both high-level semantics of pattern matching and the semantics of an intermediate-level implementation language which makes it, in principle, more scalable as we only need to modify the high-level semantics in order to synthesize the implementation of a new feature. Our evaluation on a set of small samples, partially taken from existing literature, shows, that our framework is capable of synthesizing optimal implementations in a negligible time. The in-depth stress evaluation on a number of artificial benchmarks, however, has shown the need for future improvements