Digital processing of speech signal and speaker recognition algorithm is very important for fast and accurate automatic voice recognition technology. A direct analysis of the voice signal is complex due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. The non-parametric method for modeling the human auditory perception system, Mel Frequency Cepstral Coefficients (MFCCs) is utilized as extraction technique. MFCC imitates the human hearing system; therefore it provides better recognition rates than Linear Predictive Coefficients (LPC). For the present work, work, the non linear sequence alignment known as Dynamic Time Warping (DTW) is used as features matching technique. Since voice signal tends to have different temporal rate, the alignment is important to produce better performance. This paper presents the viability of MFCC to extract features of speech signal and DTW to compare the corresponding test patterns.