In this paper, we use Hand Movement Orientation and Grasp (HMOG) sensor data to authenticate smart phone users. The way a user holds, grasps a mobile phone or touches to it are all key factors for authentication. At the moment of a user makes an event on his/her smart phone, three sensors automatically collect data about magnitude, angular speed and acceleration. Moreover, touching and holding events also produce data about pressure and coordinates. In this paper, we build four types of machine learning algorithms (decision forest, boosted decision tree, support vector machine, logistic regression) to predict user authentication. The data used in this experiment (HMOG) are collected from 100 students. We compare the results of the algorithms and for our scenario, we show that decision forest algorithm with normalized data gives best results. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of KES International.