In this paper, Principal Component Analysis (PCA) is applied to the problem of Online Handwritten Character Recognition in the Tamil script. The input is a temporally ordered sequence of (x,y) pen coordinates corresponding to an isolated character obtained from a digitizer. The input is converted into a feature vector of constant dimensions following smoothing and normalization. PCA is used to find the basis vectors of each class subspace and the orthogonal distance to the subspaces used for classification. Pre-clustering of the training data and modification of distance measure are explored to overcome some common problems in the traditional subspace method. In empirical evaluation, these PCA-based classification schemes are found to compare favorably with nearest neighbour classification.