In this paper, a hybrid electronic noses’ system (HENS) based on MOS-SAW detection units intended for lung cancer diagnosis is proposed. The MOS gas sensors are used to detect the VOC molecules with low molecular weight (LMW), and the SAW sensors are adopted for the detection of VOC with high molecular weight (HMW). Thus, the novel combination of these two kinds of gas sensors provides higher sensitivities to more of VOC species in breath than that of using only a single kind of sensor. The signals from MOS-SAW detection units are then recognized by a multi-model diagnosis method. Applying four algorithms, six models were established for diagnosis and tested by leave-one-out cross-validation method. The model by artificial neural network (ANN) was selected as the best model to analyze breath samples. 89 clinical samples were tested with MOS-SAW ANN diagnostic model, which takes the features derived from both the MOS and SAW sensors. It shows the highest sensitivity of 93.62%, and the highest selectivity of 83.37%. The study shows that, promisingly, our HENS is effective during screening of lung cancer patients, especially among the people of high risk.