With the continuous evolution of technology, mobile devices are becoming more and more important in people’s lives. In the same way, new needs related to the information provided by their users arise, making evident the need to develop systems that take advantage of their daily use. The recognition of personal activity based on the information provided by the last generation mobile devices can easily be considered as an useful tool for many purposes and future applications. This paper presents the use of information provided from two smart devices in different acquisition schemes, assessing conventional supervised classifiers to recognize personal activity by an identification of seven classes. The classifiers were trained with a generated database from eight users and were evaluated in offline mode with other two generated databases. The prediction experiments were qualified by using F1-score indicator and were compared with the native prediction from the cellphone. The obtained results presented a maximum F1-score of 100% for the first validation test and 80.7% for the second validation test.