In this study, responses of a sensor array were examined to establish a quality index model for evaluating peach quality index. The results showed that the multiple linear regression model is effective for predicting quality index, with high correlation coefficients (R2 = 0.87 for compression force; R2 = 0.79 for sugar content; R2 = 0.81 for pH) and relatively low average percentage errors (9.66%, 7.68% and 3.6%, for compression force, sugar content and pH, respectively). The feed-forward neural network also provides an accurate quality index model with high correlations (R2 = 0.90, 0.81 and 0.87 for compression force, sugar content and pH, respectively) between predicted and measured values and relatively low average percentage errors (6.39%, 6.21% and 3.13% for compression force, sugar content and pH, respectively) for prediction. These results prove that the electronic nose has the potential to become a reliable instrument to assess fruit quality index.