Saffron is the commercial name of the dried stigmas of Crocus sativus L. flower. Due to the high cost of saffron, adulteration sometimes occurs in the local market. In this study, the aroma fingerprints of saffron, saffron with yellow styles, safflower, and dyed corn stigma were detected by an electronic nose system. The features of the obtained signals from electronic nose system were extracted and used for data analysis. In this work, principal component analysis was used and the results were confirmed by back propagation artificial neural networks. The results revealed that the system can recognize the saffron adulteration satisfactorily. As a conclusion, it was found that the electronic nose could provide good separation of the saffron and adulterated one (safflower and other adulteration) as 100 and 86.87% classification accuracy, respectively, by means of artificial neural networks. The electronic nose was able to differentiate non-adulterated and adulterated saffron at higher than 10% adulteration level successfully.