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The Cyranose 320 (Cyrano Sciences Inc., USA), comprising an array of 32 polymer carbon black composite sensors, has been used to identify species of bacteria commonly associated with medical conditions. Results from two experiments are presented: one on bacteria causing eye infections and one on a new series of tests on bacteria responsible for some ear, nose, and throat (ENT) diseases. For the eye bacteria tests, pure lab cultures were used and the electronic nose (EN) was used to sample the headspace of sterile glass vials containing a fixed volume of bacteria in suspension. For the ENT bacteria, the system was taken a step closer toward medical application, as readings were taken from the headspace of the same blood agar plates used to culture real samples collected from patients. After preprocessing, principal component analysis (PCA) was used as an exploratory technique to investigate the clustering of vectors in multi-sensor space. Artificial neural networks (ANNs) were then used as predictors, and a multilayer perceptron (MLP) trained with back-propagation (BP) and with Levenberg-Marquardt was used to identify the different bacteria. The optimal MLP was found to correctly classify 97.3% of the six eye bacteria of interest and 97.6% of the four ENT bacteria including two sub-species. A radial basis function (RBF) network was able to discriminate between the six eye bacteria species, even in the lowest state of concentration, with 92.8% accuracy. These results show the potential application of the Cyranose together with neural network-based predictors, for rapid screening and early detection of bacteria associated with these medical conditions, and the possible development of this EN system as a near-patient tool in primary medical healthcare.