The mitotic spindle is a subcellular protein structure that facilitates chromosome segregation and is crucial to cell division. We describe an image processing approach to quantitatively characterize and compare mitotic spindles that have been imaged three dimensionally using confocal microscopy with fixed-cell preparations. The proposed approach is based on a set of features that are computed from each image stack representing a spindle. We compare several spindle datasets of varying biological (genotype) and/or environmental (drug treatment) conditions. The goal of this effort is to aid biologists in detecting differences between spindles that may not be apparent under subjective visual inspection, and furthermore, to eventually automate such analysis in high-throughput scenarios (thousands of images) where manual inspection would be unreasonable. Experimental results on positive- and negative-control data indicate that the proposed approach is indeed effective. Differences are detected when it is known they do exist (positive control) and no differences are detected when there are none (negative control). In two other experimental comparisons, results indicate structural spindle differences that biologists had not observed previously.