Smart phones are widely used in indoor positioning and navigation systems. Thus, it is important to know the relative position of the smart phone on the user while calculating position with RSS based systems. The accuracy of position will change within this scope. In this study, an intermediary solution is proposed to find the position of the smart phone on the user. Several algorithms are evaluated for the feature selection process. The outputs of the process are estimated via three different classifiers, namely Naives Bayes, k-Nearest Neighborhood and C4.5 decision tree. k-Nearest Neighborhood algorithm achieves 96% accuracy with the output of Correlation Feature Selection.