Within computational linguistics, the use of statistical pattem matching is generally restricted to speech processing. We have attempted to apply statistical techniques to discover a grammatical classification system from a Corpus of 'row' English text. A discovery procedure is simpler for a simpler language model; we assume a first-order Markov model, which (surprisingly) is shown elsewhere to be sufficient for practical applications. The extraction of the parameters of a standard Markov model is theoretically straightforward; however, the huge size of the standard model for a Natural Language renders it incomputable in reasonable time. We have explored various constrained models to reduce computation, which have yielded results of varying success.