A major challenge in microbial ecology is to develop reliable and facile methods of computer-assisted microscopy that can analyze digital images of complex microbial communities at single cell resolution, and compute useful quantitative characteristics of their organization and structure without cultivation. Here we describe a computer-aided interactive system to analyze the high degree of morphological diversity in growing microbial communities revealed by phase-contrast microscopy. The system, called "CMEIAS" (Center for Microbial Ecology Image Analysis System) consists of several custom plug-ins for UTHSCSA ImageTool, a free downloadable image analysis program operating on a personal computer in a Windows NT environment. CMEIAS uses various measurement features and two object classifiers to extract size and shape measurements of segmented, digital images of microorganisms and classify them into their appropriate morphotype. The first object classifier uses a single measurement feature to analyze relatively simple communities containing only a few morphotypes (e.g., regular rods, cocci, filaments). A second new hierarchical tree classifier uses an optimized subset of multiple measurement features to analyze significantly more complex communities containing greater morphological diversity than ever before possible. This CMEIAS shape classifier automatically categorizes each cell into one of 11 predominant bacterial morphotypes, including cocci, spirals, curved rods, U-shaped rods, regular straight rods, unbranched filaments, ellipsoids, clubs, rods with extended prostheca, rudimentary branched rods, and branched filaments. The training and testing images for development and evaluation of the CMEIAS classifier were obtained from 1,937 phase-contrast grayscale digital images of various diverse communities. The CMEIAS shape classifier had an accuracy of 96.0% on a training set of 1,471 cells and 97.0% on a test set of 4,270 cells representing all 11 bacterial morphotype classes, indicating that accurate classification of rich morphological diversity in microbial communities is now possible. An interactive edit feature was added to address the main sources of error in automatic shape classification, enabling the operator to inspect the assigned morphotype of each bacterium based on visual recognition of its distinctive pseudocolor, reassign it to another morphotype class if necessary, and add up to five other morphotypes to the classification scheme. The shape classifier reports on the number and types of different morphotypes present and the abundance among each of them, thus providing the data needed to compute the morphological diversity within the microbial community. An example of how CMEIAS can augment the analysis of microbial community structure is illustrated by studies of morphological diversity as an indicator of dynamic ecological succession following a nutrient shift-up perturbation in two continuously fed, anaerobic bioreactors with morphologically distinct start communities. Various steps to minimize the limitations of computer-assisted microscopy to classify bacterial morphotypes using CMEIAS are described. In summary, CMEIAS is an accurate, robust, flexible semiautomatic computing tool that can significantly enhance the ability to quantitate bacterial morphotype diversity and should serve as a useful adjunct to the analysis of microbial community structure. This first version of CMEIAS will be released as free, downloadable plug-ins so it can provide wide application in studies of microbial ecology.