One interesting part of the application of human activity recognition is sports motion recognition and classification. In recent years, many commercial wearable devices have been used for recording and supervising motion data information during sports. However, their claimed high-accuracy results but motion recognition and classification method have not been proven. This thesis project presents work special related to tennis stroke detection and classification. An automated and comprehensive tennis stroke recognition and classification method based on the inertial measuring unit sensor (accelerometer and gyroscope) and machine learning algorithm (Support vector machines) was proposed in this study. Seven tennis players with a different level of tennis skills were tested and recorded using a self-made IMU sensor system with four sensors (forearm, upper arm, trunk, and pelvis). Video footage from Playsight was manually notated as the golden standard for stroke type identification. SVMs was constructed to train the classification model to classify true shots to eight types of tennis strokes from the IMU signals. Across leave-one-out seven-fold cross-validation, the SVMs classification models were trained with data from a single IMU sensor on the forearm and upper arm with the prediction accuracies of 0.69 and 0.70 respectively. And further, both SVMs models were trained by enlarged training data, resulting in improved prediction accuracies of 0.75 and 0.77. Noticeably, the best prediction accuracy was achieved by training the SVMs classification model with fused data from the previous two sensors and with the enlarged training data. The final prediction result was 0.79. Even though there exist deficiencies such as skill level different of subjects, insufficient training datasets which may lead the results of validation and prediction less credible, the IMU sensor and SVMs machine learning algorithm still played well in the tennis stroke classification task. And we expect to have better accuracy results by feeding enough training data and using data-fusion combination of different IMU sensors to the upper extremity to SVMs classification model in future work.