Sequential pattern mining attempts to extract frequent behaviours from sequential dataset. When sequences are labeled, it is interesting to extract characteristic behaviors for each se- quence class. This task is called discriminant pattern mining. In this paper, we introduce discriminant chronicle mining. Conceptually, a chronicle is a graph whose vertices are events and edges represent quantitative time constraints between events. We also propose DCM, an algorithm dedicated to mining of discriminant chronicles. It is based on rule learning methods to extract the temporal constraints. Computational performances and discriminant power of extracted chronicles are evaluated on synthetic and real data.