Vitamin C (ascorbic acid) is a water-soluble vitamin important for maintaining biological functions in the body. People cannot synthesize vitamin C, so it needs to be intake as a dietary supplement. The most widespread form of oral nutritional supplements is the tablet because of its easy use and rapid absorption. The aim of this paper was to evaluate the applicability of near infrared spectroscopy (NIR), chemometrics and artificial neural networks (ANN) to predict the content of active substances in dietary supplements (vitamin C). Samples were prepared with different concentrations of lactose and vitamin C. In the paper, changes in electrical conductivity, total dissolved solids and pH were monitored by direct methods, depending on the concentration of lactose and vitamin C in the solutions and tablets. Vitamin C concentration was also directly determined spectrophotometrically using dichromate and permanganate. In addition to direct measurement, near-infrared spectroscopy was also used to determine the similarities and differences in the samples. For the processing of the data obtained by NIR spectroscopy, principal component analysis (PCA analysis) was used, which showed good separation based on lactose and vitamin C concentration. To establish a relationship between data and concentration, PLS regression models were developed which established linear dependence between absorbance and concentration of vitamin C. The ANN also proved to be highly precise and reliable for prediction of vitamin C concentrations in tablets.