In recent times, automation has become a crucial driver of efficiency and productivity across the industry, which strives to streamline operations and reduce human intervention. In this scenario, anomaly detection plays an essential role in maintaining systems’ integrity and ensuring the smooth functioning of automated processes and the quality of the resulting products. The present Master Thesis develops the topic of automatic anomaly detection, applying it to real-world case studies, derived from the specific needs of the Procter & Gamble Company, with which this work is in collaboration. The objective of the project is to develop algorithms able to perform anomaly detection on two different levels: in online data, coming from sensors and actuators installed on the lines, and in offline data, specifically in a database, containing all the centrelines of the working machines.