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Modern machining systems dealing with variety of products in small series demand real-time monitoring abilities that are adaptive to the sudden changes of cutting conditions. The modern computer numerical control (CNC) controllers and smart sensors are now efficient and intelligent enough to communicate with devices and process the raw data during machining. Internet of Things (IoT) and cloud manufacturing technologies provide an efficient environment for integrating and connecting knowledge, software, computing and physical parts of distributed machining equipment together. These new cloud technologies can be employed for efficient machining process monitoring and control applications. They can be used to connect machine tool and smart sensors together with cloud based monitoring capabilities via the internet networking infrastructure. Such systems that connect the cyber-world of computing, service resources and communications in the cloud with the physical devices are referred to as cyber-physical systems (CPSs) [1]. The cloud based smart monitoring systems may consequentially increase productivity, reduce machining cost and improve surface quality by efficient system adaptation. Self-monitoring, analysis, and reporting technology (Smart or S.M.A.R.T) is a monitoring system with the aim to predict or anticipate the potential process problems or failures. It includes sensing, actuation and control functions in order to analyze a process and perform actions based on the available data in an adaptive manner. The smartness of the system is often attributed to autonomous operation based on closed loop control and networking capabilities [1]. Recently, a few smart machining systems have been designed to carry out adaptive control of machine tools. Wang et al. [2] presented a concept and innovative use of adaptive smart machining based on using constant cutting force and a smart cutting tool, developed by authors. Teti et al. [3] reviewed advanced monitoring achievements in machining operations, from sensorial perception and feature extraction to cognitive decision making and action. Nowadays, surface finish monitoring control becomes increasingly important for the smooth operation of modern machine tools. A Cyber-Physical System for Surface Roughness Monitoring in End-Milling Župerl, U. – Čuš, F. Uroš Župerl* – Franci Čuš University of Maribor, Faculty of Mechanical Engineering, Slovenia