Feature extraction is a key step in the recognition of online handwritten data and is well investigated in literature. In the case of Tamil online handwritten characters, global features such as those derived from discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform have been used to capture overall information about the data. On the hand, local features such as (x; y) coordinates, nth derivative, curvature and angular features have also been used. In this paper, we investigate the efficacy of using global features alone (DFT, DCT), local features alone (preprocessed (x; y) coordinates) and a combination of both global and local features. Our classifier, a support vector machine (SVM) with radial basis function (RBF) kernel, is trained and tested on the IWFHR 2006 Tamil handwritten character recognition competition dataset. We have obtained more than 95% accuracy on the test dataset which is greater than the best score reported in the literature. Further, we have used a combination of global and local features on a publicly available database of Indo-Arabic numerals and obtained an accuracy of more than 98%.