Abstract—In this paper we present a speaker independent speech recognizer for isolated Hindi digits. Speech samples are collected from 30 individuals representing 5 distinct age groups from 15 to 40 years. For training the Hidden Markov Model (HMM) we use a total of 1000 utterances from 20 individuals. Optimal features such as MFCC, DMFCC and MFCC are used to train a HMM model. We aim to find the best combination of these features which yields the highest recognition rate along with the optimal number of hidden states of the HMM. Using MFCC and DMFCC as the feature vectors and 8 hidden states, an average recognition rate of 75% is achieved on a dataset of 500 utterances.