The aims of this study are to assess exchange rate co-movements by grouping the currencies based on similarities in their patterns, and to emphasize the importance of the trajectories of exchange rate co-movements in the exchange rate classification. Hierarchical clustering is performed with some widely used similarity measures along with the longest common subsequence (LCS) algorithm. Weekly series of twenty-one currencies were used in this study. The results show that; i) LCS performs better than the other measures and it produces comprehensible results, ii) historical and geographical factors play an important role in the co-movement of currencies. Co-movements (common trajectories) of currencies need to be taken into consideration in studies on exchange rate behavior; since these trajectories usually contain most of the information. This chapter has important implications for the analyses in the research areas of exchange rate regime choice, monetary policy implementation, and the optimum currency areas (OCA) theory.