Human-robot interaction (HRI) is a significant area of interest in robotics which has attracted a wide variety of studies in recent years. In order to provide natural human-robot interaction, robots will have to acquire the skills to detect and to integrate meaningfully information from multiple modalities. In this paper, a practical speech-controlled mobile robot car system is presented and discussed. In this study the developed Hidden Markov Model (HMM) with separate word recognition system and real-time control were obtained on a mobile robot. Mel-Frequency Cepstral Coefficients (MFCC) were applied as features for the control design of mobile robot. In the study, 270 speech commands (İLERİ=forward, GERİ=backward, DUR=stop, SAĞA=right, SOLA=left) which are collected from 54 different people were applied to a series of mathematical operations and 12 cepstral coefficients were derived. Therefore, a database was generated by 12 cepstral coefficients. Thus, HMM model was trained and tested according to database. Speech data were classified in two groups as 90% training data and 10% test data. The recognition success rate of test commands was measured 94%.