We report on a comparison of fuzzy and HMM phoneme recognizers of data Timit corpus. We aimed to check an optimal number of signal cepstral coefficients for both a1pproaches. For this purpose, we used different parameterization techniques such as MFCC, LPCC and PLP. Also, coefficient numbers has been varied from 12 to 39 including first and second derivatives and signal energy to introduce signal temporal variation. Results showed that an appropriate number of acoustic parameters lead to an extensive performance recognition for both systems.