Characterization of an unknown sample of edible oil and checking for its purity or for its possible blending with other edible oils is common problem facing the oil chemists. Commonly used methods in such cases are based on SFI, TLC and GLC. Earlier some techniques were developed utilizing the methodologies of statistical models like SIMCA. Methodologies based on artificial neural networks (ANN) were observed to be the ideal tools in many problems involving classification and prediction. A case study consisting of the GLC data of pure groundnut oil and 50%, 60%, 70%, 80% and 90% of its blends with five other commonly used edible oils viz., ricebran, mustard, sunflower, safflower and palmolein was undertaken. The training set data consists of the GLC peaks recorded at palmitic, stearic, oleic, linoleic and linolenic for the pure groundnut oil and 90% of its blend with other edible oils. The test data, on the other hand, consists of the GLC data of 50%, 60%, 70% and 80% of the blend of groundnut oil with other edible oils. The ANN model could successfully identify the constituent oils in the case of all binary blends of both training data set and test data set with 100% accuracy. Models based on multiple linear regression could successfully predict the exact percentages of the components of the composition of the constituent edible oils in the case of the binary blends.