A new method for the fast discrimination of different producing areas of olive oil by means of near infrared spectroscopy (NIRS) was developed.A relation was established between the reflection spectra and three varieties of olive oil from different places.The data set of modeling consists of a total of 90 samples of olive oil and each type consists of 30 samples.Genetic algorithms (GA),a global searching method,was applied to select the key features of the wavelengths.By the treatment with GA,the quantitative information was obtained and the number of characteristics for principal component analysis (PCA) was reduced to 9.By the treatment with PCA,the quantitative information was obtained and the number of characteristics for BP (back propagation) neural network was reduced to 6.The analysis suggests that the cumulate reliabilities of PC1 and PC2 (the first two principal components) are higher than 99%.It appeared to provide the best clustering of the different areas of olive oil and the results show that it is successful to use the GA to extract the key features of spectral wavelengths of olive oil.The first 6 principal components were used for modeling parameters of BP neural network model and the area sorts of olive oil were used for parameters of export.Three layers of neural network model were built up to predict the 30 unknown samples.The recognition rate of 100% was achieved.It can be concluded that the method is quite suitable for the fast discrimination of producing areas of olive oil and also offers a new-approach to the discrimination of producing areas of other oils.