Fast and reliable identification of microbial isolates is a fundamental goal of clinical microbiology. However, in the case of some fastidious gram-negative bacterial species, classical phenotype identification based on either metabolic, enzymatic, or serological methods is difficult, time-consuming, and/or inadequate. 16S or 23S ribosomal DNA (rDNA) bacterial sequencing will most often result in accurate speciation of isolates. Therefore, the objective of this study was to find a hypervariable rDNA stretch, flanked by strongly conserved regions, which is suitable for molecular species identification of members of the Neisseriaceae and Moraxellaceae. The inter- and intrageneric relationships were investigated using comparative sequence analysis of PCR-amplified partial 16S and 23S rDNAs from a total of 94 strains. When compared to the type species of the genera Acinetobacter, Moraxella, and Neisseria, an average of 30 polymorphic positions was observed within the partial 16S rDNA investigated (corresponding to Escherichia coli positions 54 to 510) for each species and an average of 11 polymorphic positions was observed within the 202 nucleotides of the 23S rDNA gene (positions 1400 to 1600). Neisseria macacae and Neisseria mucosa subsp. mucosa (ATCC 19696) had identical 16S and 23S rDNA sequences. Species clusters were heterogeneous in both genes in the case of Acinetobacter lwoffii, Moraxella lacunata, and N. mucosa. Neisseria meningitidis isolates failed to cluster only in the 23S rDNA subset. Our data showed that the 16S rDNA region is more suitable than the partial 23S rDNA for the molecular diagnosis of Neisseriaceae and Moraxellaceae and that a reference database should include more than one strain of each species. All sequence chromatograms and taxonomic and disease-related information are available as part of our ribosomal differentiation of medical microorganisms (RIDOM) web-based service (http://www.ridom.hygiene.uni-wuerzburg.de/). Users can submit a sequence and conduct a similarity search against the RIDOM reference database for microbial identification purposes.