Cataloging is challenging in crowded fields because sources are extremely covariant with their neighbors and blending makes even the number of sources ambiguous. We present the first optical probabilistic stellar catalog, cataloging a crowded (~0.1 sources per pixel) Sloan Digital Sky Survey r band image from M2. Probabilistic cataloging returns an ensemble of catalogs inferred from the image and thus can capture source-source covariance and deblending ambiguities. By comparing to a traditional catalog of the same image and a Hubble Space Telescope catalog of the same region, we show that our catalog ensemble better recovers sources from the image. It goes more than a magnitude deeper than the traditional catalog while having a lower false discovery rate brighter than 20th magnitude. Future telescopes will be more sensitive, and thus more of their images will be crowded. We detail our efforts to extend probabilistic cataloging to galaxies, making the method applicable to the data that will be collected in the Large Synoptic Survey Telescope era.