Activity recognition with smartphones is possible by using its internal sensors without using any external sensor. First of all, previous works and their techniques will be regarded and from these works an own implementation for the activity recognition will be derived. Most of the previous works only use the accelerometer for the activity recognition task. For this reason, this bachelor thesis analyzes the benefit of further sensors, such as the magnetic field, the linear acceleration or the gyroscope. The activity recognition is performed by classification algorithms. Decision Tree, Naive Bayes and Support Vector machines will be used. Sensor data of subjects will be collected and saved by using an own developed application. This data is needed as training data for the classification algorithms. The result is a model which represents the structure of the data. To validate the model, a test dataset will be used which is different from the training dataset. The results confirm previous works which indicated that the activity recognition task is possible by only using the accelerometer. Orientation, gyroscope and linear acceleration cannot be used for all problems of the activity recognition. Apart from that, the Decision Tree seems to be the best classification algorithm if the model has no training data of the current user.