The problem of time-series segmentation has been widely discussed and it has been successfully applied in a variety of areas including computational genomics, telecommunications and process monitoring. Nevertheless not many techniques have been devised to deal with multidimensional evolving data describing complex objects. Moreover, in many applications the resulting segments have not a description understandable to the user, and this is exacerbated in the applications with complex data. Our contribute aims to propose an algorithmic framework to segment multidimensional evolving data or multidimensional time-series and to resort to an ILP system to generate characterizations of segments close to the user. The application and the results to the realworld data are reported.