In this paper, we address some issues in developing an online handwritten character recognition(HCR) system for an Indian language script, Telugu. The number of charac- ters in this script is estimated to be around 5000. A char- acter in this script is written as a sequence of strokes. The set of strokes in Telugu consists of 253 unique strokes. As the similarity among several strokes is high, we propose a modular approach for recognition of strokes. Based on the relative position of a stroke in a character, the stroke set has been divided into three subsets, namely, baseline strokes, bottom strokes and top strokes. Classifiers for the differ- ent subsets of strokes are built using support vector ma- chines(SVMs). We study the performance of the classifiers for subsets of strokes and propose methods to improve their performance. A comparative study using hidden Markov models(HMMs) shows that the SVM based approach gives a significantly better performance.