Devanagari is a script used for several major languages such as Hindi, Sanskrit, Marathi and Nepali, and is used by more than 500 million people. Unconstrained Devanagari writing is more complex than English cursive due to the possible variations in the order, number, direction and shape of the constituent strokes. An on-line pen-computing environment has numerous applications in providing an easy human interface for a complex script like Devanagari. A Devanagari character recognition experiment with 20 different writers with each writer writing five samples of each character in a totally unconstrained way has been conducted. An accuracy of 86.5% with no rejects is achieved through the combination of multiple classifiers that focus on either local on-line properties, or global off-line properties. Further improvements in performance are expected by using word-level contextual information. We are also exploring the use of writer dependent models to improve the recognition accuracy.