AbstractThe current state in shape analysis is distinguished by a number of characterization methods, but the great variety of specific shapes complicates the selection of parameters that are relevant for a particular problem. Therefore, the preferred approach is to characterize single particles "free of presupposition" and to select technologically relevant parameters using cluster and discriminance algorithms.Parameter vectors including elongation, bulkiness, fractal dimension and area‐equivalent diameter are calculated on the basis of image analysis. First applications to bacteria and agricultural freestuffs exemplify the concept and illustrate that technologically relevant particle shape analysis permits the classification of single particles and the quantification of property function