The goal of this paper is the application of the Genetic Algorithms (GAs) to the Automatic Speech Recognition (ASR) domain at the acoustic sequences classification level. Thus, we have looked for recognizing Standard Arabic (SA) stop sounds of continuous, naturally spoken, speech. We have used GAs because of their advantages in resolving complicated optimization problems, where analytic methods fail. Stop consonants represent one of the various classes of sounds in human speech. Their classification is one of the most challenging tasks in speech recognition due to their dynamic, variable context and speaker-dependent nature. In SA, there are eight stop consonants. W e have analyzed a corpus that contains several sentences composed of the eight types of stop consonants in the initial, medium and final positions, recorded by several male speakers. Since the cepstral coefficients representation is known to provide the best speech recognition performance, we have used Mel Frequency Cepstrum Coefficients (MFCCs) method to extract vocal tract coefficients. To represent temporal variations in the speech signal, the first and second derivatives of both MFCCs and energy are added to the set of static parameters. The acoustic segments classification and the GAs have been explored. Among a set of classifiers like Bayesian, likelihood and distance classifier, we have used the distance one. It is based on the classification measure criterion. So, we formulate the supervised classification as a function optimization problem and we have used the decision rule Mahalanobis distance as the fitness function for the GA evaluation. We report promising results with a classification recognition accuracy of 89%.