There is an increasing demand world-wide for the application of intelligent, fast and inexpensive measurement systems in clinical diagnosis. Electronic noses, which are used for characterising complex vapours and aromas, may be useful for detection of bacterial contamination or diagnosis of infections, if minimal standards of selectivity and sensitivity can be met. Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. An electronic nose based on chemoresistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with urinary tract infections, gastrointestinal and respiratory infections in a Public Health Laboratory environment. An intelligent unit consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on neural networks and genetic algorithms have been applied in the identification and characterisation of microbial pathogens. The concept of fusion of multiple classifiers dedicated to specific feature parameters has been also adopted in this study. The experimental results confirm the validity of the presented methods.