While being developed, plant somatic embryos change shape and increase size. An effective kinetic description of growth and development of somatic embryos is important for process scale-up and optimization. An essential component of such a kinetic description is the developmental characterization of the individual embryos present in culture. Embryo morphological data obtained by image processing techniques were transformed into size and size-independent morphological descriptors. Qualitative relations between the descriptors and geometric properties of the embryos were established to interpret the results. For training, a branch-and-bound search technique was used to search for optimal subsets of descriptors, as determined by member clustering and class separability properties evaluated from within-class and between-class scatter matrices. In the classification mode, individuals were identified using a voting nearest neighbor classifier. This nonparametric nearest-neighbor classifier was trained on optimal projections of the feature space established from developmental stage discrimination (branch-and-bound algorithm). Using a test population, normal and abnormal embryos and callus were assigned to six morphological classes. The image-analysis-based classification was in 80-90% agreement compared to the results obtained through visual classification by an experienced operator.