In the development stages of a Digital twin of production assets, especially machine tools, real time process monitoring and data gathering proves to be vital. Having a monitoring system that monitors and updates the operators or managers in real time, helps improve productivity in terms of reducing downtime through predictive/preventive analytics and by incorporating in process quality assessment capabilities. When it comes to Real time monitoring of machine tools andprocesses, sensor technologies have proven to be the most effective and widely researched. Years of research and development have paved the way for many smart sensor technologies that come both in built with the machine tools as well as external applications. However, these technologies prove to be expensive and complicated to implement especially for Small and Medium Enterprises. This thesis focuses on evaluating and testing a simple, cost-efficient monitoring system using inexpensive sensor technologies that would help optimize an existing Digital twin setup for machine tools for Small and Medium Enterprises. Experiments with a 5 axis CNC machine tool using different tools and varying operating parameters, materials were performed,and the relevant sensor data were collected, mapped, analysed for accuracy and benchmarking. The thesis also evaluates the integration of this data with the information already collected from other sources, improve existing data reliability, and provides guidelines on this could be transformed usefully to create more value to SME’s.