Nowadays, wearable sensors are used for many applications such as healthcare, animation, sports, to name but a few. In this study, they are used to recognize volleyball activities such as digs, blocks, serves and spikes. These activities are normally followed by statisticians on the field, their presences and frequencies are noted by them to be recorded at the match report. This study focuses on automating this procedure and identifying/recognizing them using wearable sensors. Five Xsens MTw Awinda sensors are used to collect data from 10 volleyball players (5 women and 5 men) who are between 19-21 ages and have 3-12 years of experience as an active player in volleyball. In this thesis, optimum number of sensors and their locations, effects of combinations of different features such as minimum, maximum values, means and variances of the raw data, impacts of combinations of different sub sensors such as accelerometer, gyroscope and magnetometer on the 4-class&10-class classification average accuracies are investigated. Two classification algorithms are applied with two different cross validation methods: For both cross validation methods, LDA (Linear Discriminant Analysis) produced better average accuracies than KNN (K Nearest Neighbor) where k value is taken as 5. The average accuracies for 4-class and 10-class classifications are respectively 99.56% and 89.56%. However, these results are respectively 92.39% and 66.08% for KNN (k=5).