We propose a new framework for the modelling of sequences that generalizes popular models such as hidden Markov models. Our approach relies on the use of relational features that describe relationships between observations in a sequence. The use of such relational features allows implementing a variety of models from traditional Markovian models to richer models that exhibit robustness to various kinds of deformation in the input signal. We derive inference and training algorithms for our framework and provide experimental results on on-line handwriting data. We show how the models we propose may be useful for a variety of traditional tasks such as sequence classification but also for applications more related to diagnosis such as partial matching of sequences.