The state-of-the-art in automatic speech recognition is distinctly Markovian. The ubiquitous ‘beads-on-a-string’ approach, where sentences are explained as a sequence of words, words as a sequence of phones and phones as a sequence of acoustically stable states, is bound to lose a lot of dynamic information. In this paper we show that a combination with example-based recognition can be used to recapture some of that information. A new approach to combine Hidden Markov Model (HMM) and phone-example-based continuous speech recognition is presented. Experiments show that the combination outperforms the HMM recognizer, and indicate that adding long-span information is especially beneficial.