Nowadays, Automatic speech recognition (ASR) technology comes as the popular innovation in human machine interaction. This technology allows a computer to recognize the spoken words and convert them to text data. In designing the computer systems that recognize spoken words, one of the challenging tasks is to be recognized spoken Myanmar digits. In this paper we focus on recognizing Myanmar digits spoken by normal voice and whispered voice. Myanmar digits recognition system for both types has been developed by using Hidden Markov Model in HTK tools and Mel Frequency Cepstral Coefficients (MFCC) technique has been used to convert the speech waveform into a set of feature vectors for recognizing the vocalization of a word. In our experiments, HMM-based acoustic and language models are used to evaluate the performance of speech recognizer for both speaker dependent and speaker independent. According to the experimental results, the performance of speaker dependent speech recognition system for normal voice and whispered voice are 90% and 88.7% respectively. The performance of speaker independent speech recognition system for normal voice and whispered voice are 67.3% and 65.7% respectively. We found that the performance of both type of speaker dependent is higher than those of speaker independent.