Current clinical diagnostics are based on biochemical, immunological or microbiological methods. However, these methods are operator dependent, time-consuming and expensive and require special skills, and are therefore not suitable for point-of-care testing. Developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. In this paper, the potential of an electronic nose as a monitoring tool in clinical microbiology is investigated through two case studies. Initially, an electronic nose based on chemoresistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with pathological infections in a Public Health Laboratory environment, while in a later stage, analysis was carried out for urinary tract infection-suspected cases incubated in a volatile generation test tube system for 4–5 h. Two issues have been considered the application of an advanced wavelet neural network and the concept of the fusion of multiple classifiers dedicated to specific feature parameters. The adopted wavelet neural network incorporates a “product operation” layer between wavelet functions and output layers, while the connection weights at output layer have been replaced by a local linear model. This study has shown the potential for early and fast detection of microbial contaminants in clinical samples utilising advanced learning-based algorithms and electronic nose technology.