Cell migration plays an essential role in a wide variety of physiological and pathological processes. Analysis of cell tracks at high temporal and spatial resolution using microscopy experiments provides a powerful tool to quantify cell movement and to disclose the interactions of cells within their environment. The most common characterization of cell tracks is typically performed at the level of the cell population and involves computing ensemble-averaged quantities, such as the diffusion coefficient  as obtained from the slope of the time dependent mean squared displacement. However,  in order to capture the many different phases of cell migration, ensemble-averaged quantities at the population level can be misleading. In this study, we introduce a novel approach to perform an automated characterization and parameter-free classification of cell tracks at the level of single-cells that avoids the absolute cell positions in the biological sample and the relative temporal offsets between cell tracks being integrated out. To accomplish this, we developed staggered measures based on the equivalent consideration of all possible segments of the track: (i) confinement ratio corresponding to the ratio of the displacement between two time points over the length of the cell track between these time points, (ii) volume asphericity characterizing the deviation of the cloud of uncorrelated cell positions in a track segment from a spherical volume, (iii) outreach ratio corresponding to the maximal displacement within a track segment over its length, and (iv) displacement ratio measuring the ratio of the displacement between the start and end points of a track segment over the maximal displacement contained within this segment. The charming aspect of the staggered measures is the possibility to detect and quantify the transient dynamical changes in a single cell track which may arise during the migration. By the combination of these quantities, we can classify cell tracks into sub-populations with common properties. We demonstrate for a population of synthetic cell tracks generated in silico as well as for in vitro neutrophil tracks obtained from microscopy experiment that the information contained in the track data is fully exploited in this way and does not require any prior knowledge, which keeps the analysis unbiased and general. The identification of cells that show the same type of migration behavior within the population of all cells is achieved via agglomerative hierarchical clustering of cell tracks in the parameter space of the averaged staggered measures. Mapping clusters of cell tracks with similar migration behavior back into a system’s spatial environment allows drawing conclusions on the structure and morphology of biological samples. The general formulation of our approach promotes its broad application to tracks of arbitrary objects and its straightforward extension with regard to other staggered measures that may further improve the results on the automated characterization and parameter-free classification of tracks.