The paper investigates the importance of measuring and interpreting performance indicators of machine tools and their impact on maintenance, productivity and costs. The goal of the research was to develop a deeper understanding of this topic and provide guidelines for optimizing maintenance and reducing costs. The hypotheses set at the beginning of the research were analyzed in detail. The first hypothesis confirms that the monitoring of relevant performance indicators enables timely intervention and optimization of machine tool maintenance, which results in a reduction of machine inactivity and an increase in productivity. The second hypothesis highlights the importance of the correct interpretation of measurement indicators in the maintenance of machine tools, because it provides valuable information about the condition of the machines and the need for intervention, which leads to the extension of the working life of the machines and the reduction of replacement costs. The third hypothesis emphasizes the usefulness of smart maintenance technologies, such as monitoring sensors and data analysis, for more efficient maintenance planning and reduction of breakdown costs. The research also focused on the difference between numerically controlled (NC) and computer numerically controlled (CNC) machines and their advantages and limitations. Parts of machine tools, such as the drive, transmission, executive and control parts, and their mutual connection and influence on the overall functionality of the machines were also investigated. Machine tool maintenance methods, such as corrective, preventive and predictive maintenance, are also studied, emphasizing their advantages and disadvantages. Through comprehensive analysis, the research identified best practices in machine tool maintenance and provided guidelines for improving performance and reducing maintenance costs. We learned that monitoring performance indicators is key to optimizing maintenance, correct interpretation of measurement indicators provides valuable information, and the implementation of smart maintenance technologies enables more efficient planning of interventions. These results have a significant application in the industry, allowing to improve productivity, reduce maintenance costs and extend the working life of machine tools.