The aim of the article is to investigate the optical properties of Bulgarian honey in regard to the potential of honey discrimination on the base of its botanical origin. Samples from three types of honey (acacia, linden, and honeydew) are measured by a fluorescence spectrometer recording emission from 350 to 800 nm with excitation at 370, 395 and 405 nm. A combination of fluorescence emission spectra with some colorimetric parameters (CIELab) is used as input data of three types of honey classifiers: the first two are based on linear and quadratic discriminant analysis, and the third one uses an artificial neural network. The neural classifier is realized as a multilayered perceptron with backpropagation learning algorithm. Principal components analysis (PCA) is used for reducing the number of inputs and for a proper visualization of the experimental results. The comparative analysis of the three classifiers is based on leave-one-out-cross validation test carried out in MATLAB environment.