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Avalanche formation and remote sensing of snow water equivalent are dependent on snow stratigraphy and grain type. Time-intensive manual snow pit profiles are currently used to determine snow pack properties, which are highly dependent on observer skill and experience, and are inherently subjective. The Snow Micro Penetrometer (SMP) and Near InfraRed (NIR) photography are two tools that are sensitive to snow microstructure and can be used to classify the snow grains into three main types: new snow, rounds, and facets. SMP and NIR measurements were taken side-by-side at 8 manual snow pits located on Grand Mesa in Colorado during the 3rd NASA Cold Lands Processes Experiment (CLPX-III). A classification tree using SMP force and micro-structural properties, and NIR reflectance, is able to determine the three grain types with 92 to 95% correct classification accuracy. Though more data is needed to perform a robust analysis and the results here are reported as preliminary, the combination of NIR and SMP tools appears to be promising for snow grain classification.