The megatrends of urbanization, energy transition, mobility, and digitalization are leading to an increasing demand for valuable raw materials. Large deposits of these minerals, such as massive sulfides, can be found in the deep sea at depths of 1000–4000 m. The Deep Sea Sampling project aims to develop technologies for sampling massive sulfides with the lowest possible footprint under deep sea conditions. Additionally, efficient machines for processing raw materials will help meet the growing demand. Worn tools result in reduced feed rates, interruptions in the work process, downtime, changes in product quality, and increased maintenance costs. Therefore, a tool wear monitoring system for mining and processing machines is extremely important for an efficient work process. Optimization goals for machines in the mining sector include increasing throughput and improving product quality, taking into account the varying properties of the raw materials. Fast feedback on the product size range would allow for control optimizations to achieve both energy efficiency and an optimized product size range. Methods for determining the particle size distribution and detecting wear on tools of mineral extraction and processing machines are being investigated in this research. Tests to prove the basic concepts are conducted on selected test benches. Furthermore, various methods of software evaluation are being examined.