The formation of bacterial colonies and biofilms requires coordinated gene expression, regulated cell differentiation, autoaggregation, and intercellular communication. Therefore colonies of bacteria have been recognized as multicellular organisms or "superorganisms." It has consequently been postulated that the phenotype of colonies formed by microorganisms can be automatically recognized and classified using optical systems capable of collecting information related to cellular pattern formation and morphology of colonies. Recently we have reported a first practical implementation of such a system, capable of noninvasive, label-free classification and recognition of pathogenic Listeria species. The design employed computer-vision and pattern-recognition techniques to classify scatter patterns produced by bacterial colonies irradiated with laser light. Herein we report our efforts to extend this system to other genera of bacteria such as Salmonella, Vibrio, Staphylococcus, and E. coli. Application of orthogonal moments, as well as texture descriptors for image feature extraction, provides high robustness in the presence of noise. An improved pattern classification scheme based on an SVM algorithm provides better results than the previously employed neural network system. Low error rates determined by cross-validation, reproducibility of the measurements, and overall robustness of the recognition system prove that the proposed technology can be implemented in automated devices for bacterial detection.