This paper compares a classic digital image processing approach to trace particles in laboratory x-ray micro-computed tomography (µCT), which is based on a combination of random sample consensus (RANSAC) algorithm and least squares ellipse fitting (LSF), with an approach based on artificial neural networks (ANNs). In order to be able to perform the comparison, dynamic experiments were carried out in a laboratory microtomography facility. During active scans with a duration of 30–75 s several sedimentation experiments have been carried out with an exposure time of 0.13 s/projection. Through the movement of the particles during the scan curved motion artefacts in the reconstructed data occur, where the vertex marks the coordinate of the particle. It could be shown that both approaches enable the tracing of particles in laboratory x-ray µCT with deviations from a manually evaluated result of exemplarily 1.25% for the conventional digital image processing (CDIP) and 0.48% for the ANN. It was found that ANNs are able to identify particle positions in non-symmetrical motion artefacts, appearing around the first and last position of the particles in the scan, allowing an extension of the motion range of the particles that can be evaluated. Both methods have their advantages and disadvantages. Due to the high complexity and size as well as partly black box structures of neural networks, they are not 100% comprehensible whereas conventional image processing is 100% transparent and understandable. But because of the complexity of the tracing of particles, the CDIP code offers many parameters that can be set, which is why the application is therefore slightly more complex.