Famous artists' paintings, in general, allow for a large number of forgeries. In the work of a great Brazilian painter, Candido Portinari, we try to detect fake works through their image. To reach classifying results we must extract from digitalized images features that distinguish Portinari's original paintings from the false ones. So, it has been noted that the degree of variation of gray tones in a brush stroke reflects each painter's particular style on the painting. It is a feature that can be easily detected by the power spectrum of the detail image. However, as power spectra have large amounts of data, we use just some significant values of them for the classification. These selected pixels of the spectrum image are in a line which is perpendicular to the direction the brush passed, i.e., they indicate the variation of gray tones in the brush stroke. The data described previously are used as input for further classification by a backpropagation neural network. This neural net was exhaustively trained and has topology and parameters appropriate to the problem. Two output units indicate the major result: original or false.