This thesis investigates the automatic assessment of essays written by Japanese low level learners of English as a second language. A number of essay features are investigated for their ability to predict human assessments of quality. These features include unique lexical signatures (Meara. Jacobs & Rodgers, 2002), distinctiveness, essay length, various measures of lexical diversity, mean sentence length and some properties of word distributions. Findings suggest that no one feature is sufficient to account for essay quality but essay length is a strong predictor for low level learners in time constrained tasks. Combinations of several features are much more powerful in predicting quality than single features. Some simple systems incorporating some of these features are also considered. One is a two-dimensional 'quantity/content' model based on essay length and lexical diversity. Various measures of lexical diversity are used for the content dimension. Another system considered is a clustering algorithm based on various lexical features. A third system is a Bayesian algorithm which classifies essays according to semantic content. Finally, an alternative process based on capture-recapture analysis is also considered for special cases of assessment. One interesting finding is that although many essay features only have moderate associations with quality, extreme values at both ends of the scale are often very reliable indicators of high quality' or poor quality essays. These easily identifiable high quality or low quality essays can act as training samples for classification algorithms such as Bayesian classifiers. The clustering algorithm used in this study correlated particularly strongly with human essay ratings. This suggests that multivariate statistical methods may help realise more accurate essay prediction.