Near Infra-Red Spectroscopy (NIRS) is a rapid and cost-effective method widely used for the determination of chemical composition of agricultural products. Reflectance spectra recorded in near infra-red region for a set of samples of known composition are used for establishing calibration model by use of one of standard multivariate calibration methods, like MLR (multiple linear regression), PCR (principal component regression) or PLS (partial least squares). Multivariate calibration is a task that can be tried to be solved with artificial neural networks (ANNs) as well. The present paper is aimed at assessing the applicability of artificial neural networks as a tool for the determination of the content of main nutritional components of rapeseed meal: protein, dry mass, fibre and oil, on the basis of NIRS measurements. To the knowledge of the authors, no paper has been published on modelling of the dependence of chemical composition of rapeseed meal and NIR spectra with ANNs. Two most popular types of ANNs are tried in this work: multi-layer perceptron (MLP) and radial basis function (RBF). The obtained results show that chosen types of ANNs can provide models of performance comparable to that characterizing models built with MLR.