This paper describes a network-based speaker-independent connected-word recognition system based on Markov modelling and compares the results obtained using different basic-units (words, phonemes, diphones). The whole knowledge of the application, including syntactical and lexical descriptions and also phonological rules, is used to create a single integrated network; and the probabilistic density functions of the Markov chain are Gaussian multivariate with diagonal matrix. The recognition tests were performed on numbers from 0 up to 999 in French, recorded from 26 speakers. For whole-word basic-units, the recognition performances (percentage of numbers correctly recognized) increased up to 90% when the number of pdf per word increased up to 15. The same recognition rate was obtained with phoneme basic-units having only 5 pdf each, thus saving computations, and more than 95% was achieved using sub-phonemic description.