In this paper, we evaluate the use of an electronic nose (EN) containing 13 conducting polymer gas sensors to discriminatebetween patterns of volatile organic compounds (VOCs)emitted by plants. The VOC patterns examined were produced by tomato, cucumber and pepper plants under both healthy and infected or infested conditions. Leaves from theplants were subjected to mechanical damage or pest anddisease attacks (i.e. spider mites infested or mildew infected)and others were judged against undamaged healthy leaves.Support vector machines (SVMs) with linear, polynomialand Gaussian radial basis function (RBF) kernels were used to process and classify the raw data collected. The SVM illustrated an ability to discriminate between different VOC patternsand hence was able to classify correctly the infectedleaves using the EN data. The results indicate that the array of 13 EN gas sensors can discriminate among VOC patternsfrom undamaged and artificially damaged leaves of the threeplant species. This study demonstrates the potential application of such an EN technology coupled with suitable pattern recognition and signal processing methods to be used as a real time pest and disease detection system in the greenhouse environment.