Virus recognition by visual examination of electron microscope (EM) images is time consuming and requires highly trained and experienced medical specialists. For these reasons, it is not suitable for screening large numbers of specimens. The objective of this research was to develop a reliable and robust pattern recognition system that could be trained to detect and classify different types of viruses from two-dimensional images obtained from an EM. This research evaluated the use of radial spectra of higher order spectral invariants to capture variations in textures and differences in symmetries of different types of viruses in EM images. The technique exploits invariant properties of the higher order spectral features, statistical techniques of feature averaging, and soft decision fusion in a unique manner applicable to the problem when a large number of particles were available for recognition, but were not easily registered on an individual basis due to the low signal to noise ratio. Experimental evaluations were carried out using EM images of viruses, and a high statistical reliability with low misclassification rates was obtained, showing that higher order spectral features are effective in classifying viruses from digitized electron micrographs. With the use of digital imaging in electron microscopes, this method can be fully automated.