We have applied a statistical protocol based on principal component analysis, clustering methods, and discriminant analysis for the identification of sperm subpopulations in computer-assisted sperm analysis (CASA) data. Samples were obtained from the cauda epididymis of 11 Iberian red deer and cryopreserved following a standard protocol. Motility by CASA was analyzed just after sperm recovery, just before freezing, and after thawing, and eight motility descriptors for each individual spermatozoon were recorded. Sperm viability and acrosomal status were also assessed. Subpopulation analysis was performed in four sequential steps: principal component analysis using the eight motility descriptors; nonhierarchical clustering analysis (k-means) using the first two principal components; hierarchical clustering analysis (UPGMA); and selection of the final number of clusters. Three clusters were obtained for each motility analysis: slow and nonlinear; rapid and linear; and rapid, high ALH, nonlinear. We detected variations in the clusters between treatments (initial, prefreezing and postthawed). Indeed, motility increased and linearity decreased in the prefreezing analysis. A discriminant analysis isolated three descriptors that were used again in the same statistical analysis, giving four clusters that resembled the pattern found in the first classification. We also performed a clustering analysis of the males according to prefreezing/postthawed variation of total motility, viability, and acrosomal status. The proportion of the linear subpopulations in the prefreezing treatment, in both clustering analyses, correlated positively with postthawed viability recovery. Our results show that clustering analysis of CASA data gives useful and practical information that is not obtained by conventional sperm analysis.