Three-dimensional (3-D) snow analysis techniques provide comprehensive and accurate snow microstructure data. Nevertheless, there remains a requirement for less elaborate methods for snow characterization, as numerical snow models such as SNOWPACK are presently based on two-dimensional (2-D) grain analysis. We present a detailed assessment of various methods and shape descriptors used for snow characterization from digitized images. Dendricity, the ratio of the square of grain perimeter to its area, allows distinction between new and old snow while sphericity distinguishes between faceted and rounded grains. The concept of sphericity is based on curvature, yet another powerful shape descriptor. However, curvatures obtained from images of disaggregated snow grains depend on both resolution and methods chosen. We compared the standard parabola method with a cubic smoothing spline approach for curvature measurement. Applying both methods to parametrically generated shapes, descriptor values were compared with their analytical counterparts. The spline method was found to be able to measure a wider range of curvatures accurately, but both methods suffered from a filtering effect. Although some descriptor errors were as high as 50%, a method for effectively outlining snow grains was found. As well as assessing the classification potential of 2-D analysis on full samples, new descriptors were also investigated.