Bacterial infections are significant causes of serious human health problems. Different bacterial pathogens might have similar symptoms, and it is important to detect the cause of the infection early in order to enable effective treatment. In many infections, a mixture of different bacteria might exist in the same tested sample. To treat such infections effectively, it is very important to identify the various types of infecting bacteria in the sample. In this pioneering study, we examined the potential of Fourier transform infrared (FTIR) microscopy for accurate identification and differentiation between different bacteria in experimentally mixed samples of bacteria in the genus level in time span of about 30 min. We have measured the FTIR spectra of bacteria in pure form as well as in various mixtures. Principal components analysis (PCA) was applied on the spectra of various classes, followed by linear discriminant analysis (LDA) as a linear classifier. Our results show that it is possible to differentiate between mixed categories of bacteria with high rates of success. The classification rate was higher when the mixed samples included Gram-positive and Gram-negative types. When the mixing range was comparable ([0.5,0.5] and [0.6,0.4]), a classification success rate > 95% was achieved using only the first 20 PCs.