The choice of shape metrics is important to effectively identify three-dimensional targets. The performance (expressed as a probability of correct classification) of four metrics using point clouds of military targets rendered using Irma, a government tool that simulates the output of an active ladar system, is compared across multiple ranges, sampling densities, target types, and noise levels. After understanding the range of operating conditions a classifier would be expected to see in the field, a process for determining the upper-bound of a classifier and the significance of this result is assessed. Finally, the effect of sampling density and variance in the position estimates on classification performance is shown. Classification performance significantly decreases when sampling density exceeds 10 degrees and the voxelized histogram metric outperforms the other three metrics used in this paper because of its performance in high-noise environments. Most importantly, this paper highlights a step-by-step method to test and evaluate shape metrics using accurate target models.