For a system to be able to generate realtime accompaniment to previously unknown songs, it must predict their harmonic development, i.e. the chords to be played. We claim that such a system must combine long-term experience, to identify typical chord sequences (e.g. II–V and II–V–I), with ‘on-the-fly’ adaptation to track-recurrent structures (e.g. choruses and refrains) of the particular song being played. We have implemented a prediction system using a neural network model that encompasses prior knowledge about typical chord sequences. The results achieved are very encouraging, and rather better than those reported in the literature. However, our predictor could not adapt its behaviour to the idiosyncrasies of each song, since online learning is difficult in neural networks. In this paper, we propose an extension to our previous work by the inclusion of a rule-based sequence tracker, which detects recurrent chord sequences while the song is being performed. We show that this hybrid model, which combines a neural network predictor with a rule-based sequence tracker, improves the system's performance.