This work is frameworked within the development of phoneme recognition systems and seeks to establish whether the incorporation of information related to the movement of the articulators helps to improve the performance thereof. For this purpose, a pair of systems is compared and developed, where the acoustic model is obtained from training hidden Markov chains. The first system represents the voice signal by Mel Frequency Cepstral Coefficients; the second uses the same Cepstral coefficients but together with articulatory parameters. The experiments were conducted on the MOCHA-TIMIT database. The results show a significant increase in the system´s performance by adding articulatory parameters compared to that based only on Mel Frequency Cepstral Coefficients