All domains of science and technology produce large and hetero-geneous data. Although much work has been done in this area, min-ing such data is still a challenge. No previous research targets the mining of heterogeneous multidimensional sequential data. In this work, we present a new approach to extract heterogeneous multidi-mensional sequential patterns with different levels of granularity by relying on external taxonomies. We show the efficiency and interest of our approach with the analysis of trajectories of care for colorectal cancer using data from the French casemix information system.