This thesis explores the adoption of different neural networks to address the task of Facial Expression Recognition (FER) on images. FER is an approach belonging to the Computer Vision and Pattern Recognition field aimed at identifying the emotion felt by a subject relying on her/his image- or video-based facial data. Facial expressions are a type of nonverbal communication, hence FER has applications in healthcare, education, criminal detection, and marketing. One of the challenges in FER is the inherent variability in how different individuals express their emotions. People may exhibit emotions differently and often blend multiple emotions simultaneously (e.g., happiness and surprise).