Life course data often consists of multiple parallel sequences, one for
each life domain of interest. Multichannel sequence analysis has been
used for computing pairwise dissimilarities and finding clusters in this
type of multichannel (or multidimensional) sequence data. Describing
and visualizing such data is, however, often challenging. We propose an
approach for compressing, interpreting, and visualizing the information
within multichannel sequences by finding (1) groups of similar
trajectories and (2) similar phases within trajectories belonging to the
same group. For these tasks we combine multichannel sequence analysis
and hidden Markov modelling. We illustrate this approach with an
empirical application to life course data but the proposed approach can
be useful in various longitudinal problems.