Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies. When compared to hand specimens, thin sections of rocks provide better and more accurate means for analysis of mineral distribution and percentage, pore space analysis, and cement composition. Because of the rich information they contain, thin section data are commonly used not only by the mining and petroleum industry, but by the academic community as well. Most petrographic analysis relies on visual inspection of rock thin sections under a microscope, a task that is laborious even for experienced geologists. Large projects with a tight time frame requiring the analysis of a large amount of thin sections may require multiple petrographers, thereby risking the introduction of inconsistency in the analysis. To address this challenge, we explore the use of deep convolutional neural networks (CNN) as a tool that can allow the petrographer to analyze and classify more samples in a consistent manner. Unlike previous studies using deep learning models trained on large volumes of thin section data, we make use of transfer learning based on robust and reliable CNN models trained with a large amount of non-geological images. With a much smaller number of labeled thin sections used in training followed by “fine-tuning” we are able to construct convolutional neural networks that achieve low error levels (<5% when images of same quality are used for training and testing) in thin section classification. While becoming widely accepted as a useful tool in the biological and manufacturing disciplines, CNN is currently underutilized in the geoscience community; we foresee an increase of use of such techniques to help accelerate and quantify a wide variety of geological tasks.