Finite-state models are used to implement a handwritten text recognition and classification system for a real application entailing casual, spontaneous writing with large vocabulary. Handwritten short paragraphs are to be classified into a small number of predefined classes. The paragraphs involve a wide variety of writing styles and contain many non-textual artifacts. HMMs and n-grams are used for text recognition and n-grams are also used for text classification. Experimental results are reported which, given the extreme difficulty of the task, are encouraging.