The field of electronic noses, electronic instruments capable of mimicking the human olfactory system, has developed rapidly in the past ten years. There are now at least 25 research groups working in this area and more than ten companies have developed commercial instruments, which are mainly employed in the food and cosmetics industries. Most of the work published to date, and commercial applications, relate to the use of well established static pattern analysis techniques, such as principal components analysis, discriminant function analysis, cluster analysis and multilayer perceptron based neural networks. The authors first review static techniques that have been applied to the steady-state response of different odour sensors, e.g. resistive, acoustic and FET-based. Then they review the emerging field of the dynamic analysis of the sensor array response. Dynamic signal processing techniques reported so far include traditional parametric and nonparametric ones borrowed from the traditional field of system identification as well as linear filters, time series neural networks and others. Finally the authors emphasise the need for a systems approach to solve specific electronic nose applications, with associated problems of sensor drift and interference.