N-grams are very popular in automatic speech recognition (ASR) systems (Young et al., 2005), (Lamere et al., 2004), (Whittaker & Woodland, 2003), (Hirsimaki et al., 2009). They have been found as the most effective models for several languages. N-grams calculated by us will be used for the language model of a large vocabulary Polish ASR system and other outside application, first of them being SnapKeys virtual keyboard. Our earlier results and process of collecting statistics were described already (Ziolko, Skurzok & Ziolko, 2010). In this chapter we want to describe a complete model and its applications. Creating a large vocbulary model of Polish is a difficult task because there are fewer Polish text corpora then for English. What is more, Polish is very inflected in contrast to English. The rich morphology causes difficulties in training language models due to data sparsity. Much more text data must be used for inflected languages than for positional ones to achieve the model of the same efficiency (Whittaker & Woodland, 2003).