Multidirectional imaging flow cytometry (mIFC) extends conventional imaging flow cytometry (IFC) for the image-based measurement of 3D-geometrical features of particles. The innovative core is a flow rotation unit in which a vertical sample lamella is incrementally rotated by 90 degrees into a horizontal lamella. The required multidirectional views are generated by guiding all particles at a controllable shear flow position of the parabolic velocity profile of the capillary slit detection chamber. All particles pass the detection chamber in a two-dimensional sheet under controlled rotation while each particle is imaged multiple times. This generates new options for automated particle analysis. In an experimental application, we used our system for the accurate classification of 15 species of pollen based on 3D-morphological information. We demonstrate how the combination of multi directional imaging with advanced machine learning algorithms can improve the accuracy of automated bio-particle classification. As an additional benefit, we significantly decrease the number of false positives in the classification of foreign particles, i.e. those elements which do not belong to one of the trained classes by the 3D-extension of the classification algorithm.