In this study, two hydroxycinnamic acids and two hydroxylated benzoic acids, namely 4-hydroxycinnamic acid (p-coumaric acid), 4-hydroxy-3,5-dimethoxycinnamic acid ( sinapinic acid), 3,4-dihydroxy benzoic acid (vanillic acid) and 3-hydroxy-4-methoxy benzoic acid (izovanillic acid), were titrated potentiometrically using tetrabuthylammonium hydroxide in 2-propanol under a nitrogen atmosphere at 25 degreesC. An artificial neural network (ANN) was applied for data treatment as a multivariate calibration tool in a potentiometric acid-base titration. The artificial neural network trained by the back-propagation learning was used to model the complex non-linear relationship between the concentration of p-coumaric acid (HpC), sinapinic acid (HS), vanillic acid (HV), and izovanillic acid (HiV), and the milivolt (mV) of solutions at different volumes of the added titrant. The principal components of the mV matrix were used as the input of the network. The optimized network predicted the concentrations of acids in synthetic mixtures. The results showed that the ANN used can proceed the titration data with an average relative error of less than 4.18%.