Stylometric analysis in text classification is most often used in authorship attribution studies. This thesis used a machine learning algorithm, the Naive Bayes Classifier, in a text classification task comparing stylometric and lexical features. The texts were extracted from the Project Gutenberg website and were comprised of three genres: detective fiction, fantasy, and science fiction. The aim was to see how well the classifier performed in a supervised learning task when it came to discerning genres from one another. R was used to extract the texts from Project Gutenberg and Python script was used to run the experiment. Approximately 1978 texts were extracted and preprocessed before univariate filtering and tf-idf weighting was used as the lexical feature while average sentence length, average word length, number of characters, number of punctuation marks, number of uppercase words, number of title case words, and parts-of-speech tags for nouns, verbs, and adjectives were generated as the feature sets for the topic independent stylometric features. Normalization was performed using the ℓ² norm for the tf-idf weighting, with the ℓ² norm and z-score standardization for the stylometric features. Multinomial Naive Bayes was performed on the lexical feature set and Gaussian Naive Bayeson the stylometric set, both with 10-fold cross-validation. Precision was used as the measure by which to assess the performance of the classifier. The classifier performed better in the lexical features experiment than the stylometric features experiment, suggesting that downsampling, more stylometric features, as well as more classes would have been beneficial.