In recent years, the need to enhance interaction between therapists and patients during rehabilitation sessions has grown, leading to the increasing use of engineering systems that incorporate algorithms for movement recognition. This work focuses on developing a classifier for finger gestures, as hand rehabilitation is crucial for post-stroke patients with motor impairments that may prevent or hinder their movements. This thesis proposes the development of a classifier that uses an Artificial Neural Network (ANN) to recognize and differentiate hand movements. Specifically, the study focuses on finger movements, such as counting, which are essential for hand functionality and rehabilitation. A preliminary study was conducted using the publicly available GRABMyo and Hyser datasets to extract valuable information for constructing a custom dataset.