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A statistical approach using sequentially principal component analysis (PCA), clustering, and discriminant analyses was developed to identify sperm morphometric subpopulations in well‐defined portions of the fresh boar ejaculate. Semen was obtained as 2 portions (the first 10 mL of the sperm‐rich fraction and the rest of the ejaculate, respectively) and frozen using a conventional protocol. Before freezing, an aliquot was used for computer‐assisted sperm morphometry analysis (ASMA). Postthaw quality was evaluated using computer‐assisted sperm analysis (CASA), and an annexin‐V/PI assay evaluated sperm membranes. The PCA revealed that 3 variables represented more than 78% of the cumulative variance in sperm subpopulations. The clustering and discriminant analyses, based on 5780 individual spermatozoa, revealed the existence of 4 sperm subpopulations. The relative percentage of these …