A technique is described which may be employed to establish a fully automated system for recognition of airborne pollen. As the different pollen taxa have only marginal differences, a full 3D volume data set of the pollen grain was recorded with a confocal laser scanning microscope (LSM) at a voxel size of about (0:2μm) 3 . This represents an intrinsic and complete data set. 14 invariant gray-scale features based on an integration over the 3D Euclidian transformation group with nonlinear kernels were extracted from these volume data sets. The classification was done with support vector machines. The use of these general gray scale features allows to easily adapt the system to other objectives (e.g., pollen of a special area) or even other objects than pollen (e.g., spores, bacteria etc.) just by exchanging the reference data base. When using a reference data base with the 26 most important German pollen taxa (385 samples), the recognition rate is 92%. With a special database for allergological purposes recognizing only Corylus, Alnus, Betula, Poaceae, Secale, Artemisia and "allergological non-relevant" the recognition rate is 97.4%.