During production of mechanical components, residual dirt collects on the surfaces, thus creating a contamination that affects the durability of the assembled products. Residual particles are currently analyzed based on microscopic 2d images. However, the particle's shape is decisive for the damage it can cause, yet can not be judged reliably from 2d data. Micro-computed tomography allows to capture the complex spatial structures of thousands of particles simultaneously. Now new methods to characterize three dimensional shapes are needed to establish 3d cleanliness analysis. In this work, unambiguously indicative geometric features are defined and it is investigated how they can yield a reliable classification in three typical classes: fibers, chips and granules. Finally, the efficiency of the proposed method is proved by analyzing samples of real dirt particles.