Hand Gesture Recognition (HGR) systems represent a powerful method for creating Human-Machine Interfaces (HMIs) with natural control. Armbands based on surface ElectroMyoGraphic (sEMG) sensors are currently the most popular technologies among HGR systems because of the characteristics of the sEMG signal, which is non-invasive and highly correlated with the gesture performed. Gesture recognition systems based on sEMG work well when the armband is placed in the same configuration used for classifier training. However, when the orientation of the wearable device changes, which is quite common in practice, the classification accuracy is drastically reduced. In this thesis, an algorithm is proposed to correct the rotation of the sEMG sensors to allow a more comfortable and immediate wearing phase for the subject using the device, without necessarily requiring the same positioning configuration used during the training phase.