This master's thesis gives an insight into the issues of predicting word sequences in natural language processing. Statistical language model is based on n-grams and it is created by language corpus processing (counting the words and word sequences). Language model assumes the existence of Markov properties among the consecutive words in a natural language. Due to the data sparsity of the corpus there is a problem of zero probability n-gram because of their nonappearance in the language corpus. In order to alleviate the problem we use smoothing techniques which will assign a specific probability to those n-grams based on the estimate of frequencies of the existing n-grams. For that purpose many smoothing algorithms have been developed, some of which have been described in this thesis. Some algorithms are not usually used independently but they are paired with some other smoothing algorithms. Depending on the implementation, the algorithms can come across some problems while smoothing and each of them has certain advantages and disadvantages and therefore they are chosen based on the type of the problem and scope of application. In order to determine the most probable word sequence based on the input data and the created language model, we use Viterbi algorithm that will give an answer which is the most probable state sequence of the hidden Markov model. In order to compare the quality of the language model the measure of perplexity is most frequently used. In this thesis the best model was the one that was created by Kneser-Ney smoothing algorithm.