The Republic of Turkey, founded in 1923, is a well-known ancient country with abundant cultural heritage and great junction location of the Asian and European Continents. Istanbul is the largest city of this country with her old name Constantinople or Byzantium. She was established by Constantinus I Magnus in A.D. 330 during the era of the Roman Empire, to serve as a well-fortified castle like Rome. Numerous attractions on historical architecture, ancient music, gourmet cuisine, and art collections can be explored and appreciated. It is our objective to build a language system that can help us to learn Turkish, to savor the beauty of her culture, and to widen our vision of travel and living. This thesis investigates the design and implementation strategies for a Turkish speech recognition system. It utilizes the speech features of the 395 common Turkish mono-syllables as the major training and recognition methodology. A training database of 12 utterances per mono-syllable is established by applying Turkish pronunciation rules. These 12 utterances are collected through reading 6 rounds of the same mono-syllables twice with different tones. The first pronounced pattern has high pitch of tone 1, while the second one has falling pitch of tone 4. Mel-frequency cepstral coefficients, linear predicted cepstral coefficients, and hidden Markov model are used as the two syllable feature models and the recognition model respectively. Under the AMD 2.8 GHz Athlon X2 2400 personal computer and Ubuntu 9.04 operating system environment, correct phrase recognition rates of 87.29% can be reached using phonotactical rules for a 3,644 vocabulary Turkish phrase database. The average computation time for the each system is less than 1.5 seconds, and the training time for the systems is about two hours.