In this work, we focus on distinguishing devices via mobile device sensors. To this end, a large dataset, larger than 25 GB, which consists of accelerometer and gyroscope sensor data from 21 distinct devices is utilized. We employ different classification methods on extracted 40 features based on various time windows from mobile sensors. Namely, we use random forest, gradient boosting machine, and generalized linear model classifiers. In conclusion, we obtain the highest accuracy as 97% from various experiments in identifying 21 devices using gradient boosting machine on the data from accelerometer and gyroscope sensors.