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Markov chain theory is a popular statistical tool in applied probability that is quite useful in modelling real-world computing applications. Over the past years; there has been grown interest to employ Markov chain theory in statistical learning of temporal (i.e. time series) data. A wide range of applications found to utilize Markov concepts; such applications include computational linguists, image processing, communications, bioinformatics, finance systems, etc .In fact, Markov processes based research applied with great success in many of the most efficient natural language processing (NLP) tools. Hence, this paper explores the Markov chain theory and its extension hidden Markov models (HMM) in (NLP) applications. This paper also presents some aspects related to Markov chains and HMM such as creating transition and observation matrices, calculating data sequence probabilities, extracting the hidden states, and profile

Statistical Markovian Data Modeling for Natural Language Processing