The recognition rate of syllables in continuous speech is hampered due to the large size of the syllable vocabulary and the confusability among them. One approach to reduce the confusability and the search space is to preclassify the syllables into a small set of equivalent classes and then perform recognition within a particular equivalent class. In this study, the syllables in a language are grouped into equivalent classes based on their consonant and vowel structure. The syllables that map onto an equivalent class are called 'cohorts'. Artificial neural network models are used to preclassify the syllables into the equivalent class to which they belong. This is followed by recognition of the syllables among the smaller number of cohorts within a class by means of hidden Markov models. The preprocessing stage limits the confusable set to the cohorts within a class and reduces the search space. This hybrid approach helps improve the recognition rate over that of a plain HMM based recogniser.