Document examiners use a variety of features to analyze a given handwritten document for writer verification. The challenge in the automatic classification of a pair of doc- uments to belong to the same or different writer, are both (i)The task of proper selection and extraction of features from the handwritten document and (ii)The use of a proper model that is capable of utilizing the true discriminatory power of these features for classification. This paper de- scribes the use of content specific skeleton based features for characters and pairs of characters (bigrams) and as- certains their discriminatory power. A triangulation skele- tonisation procedure is first used to obtain the skeleton of the character(s), and features are computed from the skele- ton. Experiments and results are conducted on content spe- cific features extracted for two most frequently occurring bigrams (th, he), and characters (d and f). A neural net- work based on a Bayesian formulation was used to ascer- tain the discriminability power of these features. To com- bine these features with previously existing writer verifica- tion features, an alternative Naive Bayes model is also de- scribed and evaluated. From the results obtained, we con- clude that bigram th has the highest discriminatory power followed by character d, f and bigram he. Also the paper highlights the significant increase in performance of writer verification( 15% more) with the use of Bayesian neural networks as against the Naive Bayes model.