Pre-processing and testing methodology plays a significant role in online handwritten character recognition. Although many researchers have proposed several pre-processing and testing methods, the effect of these techniques on the recognition and comparisons among them are ignored. In this work, experiments were conducted to analyse the effect of various pre-processing and testing methods on Kannada handwritten data. The focus of the present work is to statistically quantify the effect on recognition time and accuracy through experiments using different pre-processing methods on online handwritten data processed by the Support Vector Machine (SVM). The performance of the SVM is also compared with various other training and testing methodology. The performance of the online handwriting recognition system is affected dramatically by the various pre-processing and testing methods. Stratified tenfold cross validation showed better performance for the Kannada handwritten dataset.