A simple method that uses visible spectrophotometer data and an artificial neural network (ANN) was developed to determine edible oil color based on the L*a*b* format. The 100 oil samples consisted of nine pure oils, a sesame oil blend and three heated oils. Binary, ternary and quaternary mixtures of these 13 oils in different ratios were prepared, and absorbance values of the samples were measured in the visible region (380–700 nm). The absorbance values at wavelengths of 416, 456, 483, 537, 611 and 672 nm were used to train, validate and test the network. Strong correlations between the instrumental L*a*b*ΔE and the estimated L*a*b*ΔE were found for the test samples, with correlation coefficients (R2) of 0.989, 0.984, 0.996 and 0.992 for L*, a*, b*, and ΔE, respectively. The effects of number and combination of the wavelengths used for training of the ANN on the estimation capability of the network for the test samples were also investigated. Although a good agreement, average R2 of 0.991– 0 993 for L*a*b*, was obtained for combinations composed of three to six wavelengths with 483 and 537 nm in common, the best R2 value was obtained when all six wavelengths were used to train the ANN. The developed method is objective, cost effective and simple, and allows the color measurement with a basic visible spectrophotometer and disposable cuvettes.