The importance of suitable distance measures between intuitionistic fuzzy sets (IFSs) arises because of the role they play in the inference problem. A concept closely related to one of distance measures is a divergence measure based on the idea of information-theoretic entropy that was first introduced in communication theory by Shannon (1949). It is known that Jdivergence is an important family of divergences. In this paper, we construct J-divergence between IFSs. The proposed Jdivergence can induce some useful distance and similarity measures between IFSs. Numerical examples demonstrate that the proposed measures perform well in clustering and pattern recognition.