Safety monitoring of Industrial Control Systems (ICS) is a must for optimal operation of safe manufacturing facilities. Failures and miss-behaviours seldomly occur without prior warning, but these warnings are often subtle, requiring careful analysis of data by experienced personnel for early detection. Monitoring function allows to promptly take adequate corrective actions in order to maximize uptime and increase trust of running industrial systems. In this paper, we present two main approaches of monitoring techniques implemented in the Montimage MMT tool. The first approach is a signature-based approach, where there are safety properties to be checked on the ICS logs, and the other relies on Machine Learning (ML) to detect anomalies. Both methods have been applied to check safety on an industrial system: a crane load position system provided by ABB. Several experiments have been performed to check if the information provided by a system’s PLC is correct, guarantying the safety of the system.