Human activity recognition has been a hot topic in recent years. With the advances in sensor technology, there has been a growing interest in using smartphones equipped with a set of built-in sensors to solve tasks of activity recognition. However, in most previous studies, smartphones were used with a fixed position—like trouser pockets—during recognition, which limits the user behavior. In the position-independent cases, the recognition accuracy is not very satisfactory. In this paper, we studied human activity recognition with smartphones in different positions and proposed a new position-independent method called PACP (Parameters Adjustment Corresponding to smartphone Position), which can markedly improve the performance of activity recognition. In PACP, features were extracted from the raw accelerometer and gyroscope data to recognize the position of the smartphone first; then the accelerometer data were adjusted corresponding to the position; finally, the activities were recognized with the SVM (Support Vector Machine) model trained by the adjusted data. To avoid the interference of smartphone orientations, the coordinate system of the accelerometer was transformed to get more useful information during this process. Experimental results show that PACP can achieve an accuracy over 91%, which is more effective than previous methods.