  We use Support Vector Machines (SVMs) to classify the spin of a black hole.
The SVMs are trained and tested with a catalog of numerically generated images
of black holes, assuming disk and spherical matter models with monochromatic
emission with wavelength of 4mm. We determine the accuracy of the SVM to
classify the spin in terms of the image resolution, for which we consider three
resolutions of $16^2,~32^2$ and $64^2$ pixels. Our approach is applied to the
specific mass of the Supermassive Black Hole (SMBH) at the center of the Milky
Way. Our findings are that when the distribution is a thin disk, the accuracy
in the classification resists even the coarsest resolution with accuracy over
90\%, whereas for the spherical distribution it drops below 80\% for low and
intermediate resolutions. The results show how the distribution of matter, the
angle of vision and the image resolution influence the ease to determine the
black hole spin.
