During the past decade, the advent of new molecular techniques has led to enormous progress in biology, notably with the development of DNA microarray technology. This technology allows monitoring simultaneously the expression of thousands of genes from a given organism. DNA microarrays have been used in a variety of applications, including the characterization of bacteria in biological samples. In this thesis, two distinct DNA microarray approaches for the characterization of bacterial flora are introduced. The first approach, termed phylogenetic microarrays, consists in a probe set recognizing specific sequence signatures for each node of the bacterial phylogenetic tree. This strategy, based on sequence information, allows extending the scope of microarrays to the whole bacterial kingdom and detecting both known and unknown microorganisms in biological samples. Moreover, phylogenetic microarrays permit detection of a broader bacterial diversity compared to the classical cloning-sequencing approach. The second strategy consists in a low-density 16S rRNA gene microarray specifically designed to monitor bacterial species found in the gingival flora of African children suffering from noma, a devastating disease of unknown etiology. In an attempt to identify the causative agent(s) of noma, these two methodologies were applied for the characterization of the gingival flora of children suffering from the disease. Observations made during this study allowed exonerating Fusobacterium necrophorum, considered by some experts as the causative agent of noma. Moreover, various oral pathogens were recovered in higher abundance in noma lesions, notably Atopobium spp., Peptostreptococcus spp., Prevotella intermedia, Streptococcus pyogenes and Streptococcus anginosus. In addition, noma lesions exhibited a lower bacterial diversity compared to acute necrotizing gingivitis (ANG), thus supporting a previous hypothesis that ANG might precede acute noma. The accomplished work, while giving better insights on the bacteriology of noma, demonstrates the power of the two developed approaches to explore and systematically characterize complex microbial communities.