Facial expressions form a rich communication code, enabling the conveyance of emotions and intentions. Their classification has garnered increasing attention in research, as it has yielded promising results in various applications, such as psychological state assessment, control of human-machine interfaces, and rehabilitation of facial muscle impairments. Facial Expression Recognition (FER) can be performed through several techniques. The approach based on the surface ElectroMyoGraphic (sEMG) signal, distinct from the more commonly used computer vision strategies, offers direct insights into muscle activity, holding great potential for rehabilitative and diagnostic purposes. Preliminary studies, from a research team’s prior thesis work, have shown high average accuracy in real-time recognition of 11 facial expressions, thanks to the implementation of an Artificial Neural Network (ANN).