Abstract: The purpose of this study is the application of the Genetic Algorithms (GAs) to the supervised classification level, in order to recognize Standard Arabic (SA) fricative consonants of continuous, naturally spoken, speech. We have used GAs because of their advantages in resolving complicated optimization problems, where analytic methods fail. For that, we have analyzed a corpus that contains several sentences composed of the thirteen types of fricative consonants in the initial, medium and final positions, recorded by several male Jordanian speakers. Nearly all the world’s languages contain at least one fricative sound. The SA language occupies a rather exceptional position in that nearly half of it’s consonants is fricatives and nearly half of fricative inventory is situated far back in the uvular, pharyngeal and glottal areas. We have used Mel Frequency Cepstrum Coefficients (MFCCs) method to extract vocal tract coefficients from the speech signal. To represents 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 82%.