We consider images of boar spermatozoa obtained with an optical phase-contrast microscope. Our goal is to automatically classify single sperm cells as acrosome-intact (class 1) or acrosome-reacted (class 2). Such classification is important for the estimation of the fertilization potential of a sperm sample for artificial insemination. We segment the sperm heads and compute a feature vector for each head. As a feature vector we use the gradient magnitude along the contour of the sperm head. We apply learning vector quantization (LVQ) to the feature vectors obtained for 152 heads that were visually inspected and classified by a veterinary expert. A simple LVQ system with only three prototypes (two for class I and one for class 2) allows us to classify cells with equal training and test errors of 0.165. This is considered to be sufficient for semen quality control in an artificial insemination center.