In my pervious life, I was an astronomer and one of the big tasks many PhD students face is manual star classification. But why ask a student to do what a machine should be able to do too? In this talk, I use the spectra (stellar flux vs wavelength) of identified stars to build a classifier which will detect the identity of the stars. Using a very simple non linear SVM, I achieve an 86% accuracy with my model. The next step is to use deep neural nets to achieve a better accuracy.