In this paper I discuss an approach for building a soft text classifier based on a Hidden-
Markov-Model. The approach treats a multi-category text classification task as predicting the best
possible hidden sequence of classifiers based on the observed sequence of text tokens. This
method considers the possibility that different sections of a large block of text may hint towards
different yet related text categories and the HMM predicts such a sequence of categories. The most
probable such sequence of categories can be estimated using the Viterbi algorithm.