An anomaly is an event or data pattern that differs from the expected behavior. Anomaly de-tection is an approach that aims to detect anomalous data points, and machine learning techniquesare one of the most widely used approaches for anomaly detection. This thesis focuses on anomalydetection and classification in a modular manufacturing environment, using existing software thatsimulates an ice cream factory. Existing software contains different modules and those modules arecontrolled using controller and orchestrator. The existing software is upgraded with a new modulethat enables the injection of anomalies into the simulation process. During the development wefollowed software engineering practices for software design and development. A new dataset withnormal and anomalous behavior of the manufacturing process is generated and different supervisedmachine learning algorithms are used to detect and classify anomalies. Our objective is to generatea dataset that is completely balanced. (Additionally, we created our dataset which is composed ofthe different files). Those files contain normal behavior and abnormal behavior. Algorithms thatwere evaluated include Decision Tree, Random Forest, K-Nearest Neighbour, and Artificial NeuralNetwork. The best accuracy was achieved with the Decision Tree (without normalization) of 96%for multi-class classification problems. K-Nearest Neighbour shows the best performance for multi-class classification, resulting in 89%, and 90% for binary classification. Artificial Neural Networkgave us 91% for anomaly detection and 85% for multi-class classification. Also, Random Forestobtained 61% for binary classification and 88% for multi-class problems. Additionally, we test theperformance mode of DT with normalized data and we obtained 97% for binary classification and73% for multi-class classification.i