This thesis presents a case study conducted during my internship at Toyota Motor Europe, with a primary focus on developing an algorithm to automatically classify negative customer feedback. The aim is to ensure that such feedback is appropriately directed to the relevant division and design group within the company, also assigning a Title and a short problem description. The algorithm was trained using machine learning techniques and evaluated on a large dataset of customer feedback collected from an external company, which specializes in providing VoC (Voice of Customer) data for the automotive sector. It is worth noting that the dataset used for training was labeled manually by employees over the past years, indicating a supervised learning approach.