This study focuses on the development and evaluation of a portable electronic nose (e-nose) system for identification of different types of saffron, stigma of Crocus sativus L. (Iridaceae), based on their Volatile Organic Compounds (VOCs). The system utilizes metal oxide semiconductor gas sensors and direct head space sampling. Real-time data acquisition system, microcontroller devices and a laptop computer along with multivariate computational tools were used for development of an expert system. Eleven saffron samples from different regions were prepared for the experiments. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) as unsupervised models and Multilayer Perceptron (MLP) neural networks and Partial Least Squares (PLS) as supervised models were utilized to develop the e-nose discrimination capability. Based on the results, PCA of volatile compounds fingerprints revealed eleven distinct groups corresponding to the eleven different saffron samples. This was further confirmed by HCA which classified the groups into five distinct Quality Classes (QCs) (excellent, very good, good, medium, and poor quality) which were used as the MLP and PLS classification goals. Results of analysis showed that performance of the MLP model for prediction of saffron samples QC was better than the PLS model, with 100% success rate and high correlation coefficients of cross validation (R-2 = 0.989 and relatively low RMSE value of 0.0141). These results show that the developed system is capable of discriminating saffron samples based on their aroma and can be utilized as an aroma quality control system. (C) 2016 Published by Elsevier B.V.