We propose a method for the classification of boar sperm heads based on their intracellular intensity distributions observed in microscopic images. The image pre-processing comprises segmentation of cell heads and normalization for brightness, contrast and size. Next, we define a model distribution of head intracellular intensity of an alive cell using a set of head images assumed to be alive by veterinary experts. We now consider two other sets of cell head images, one formed by heads assumed to be alive by experts and another formed by cells which present some abnormalities in their cytoplasm densities and are considered as dead by the experts. We define a measure of deviation from the model intensity distribution and for each head image of the two test sets we compute the deviation from the model. While the distributions of deviation values for alive and dead cells overlap, it is possible to choose an optimal value of a decision criterion for single cell classification in such a way that the error made in the estimation of the fraction of alive cells in a sample is minimal. In the range [0.7,1.0] of interesting values of the fraction of alive cells, the standard deviation of the fraction estimation error for samples of 100 head images is smaller than 0.04. Thus, in 95% of the cases the value of the fraction of alive cells in a sample estimated by a veterinary expert will be within 8% of the estimation made according to the proposed method. This result satisfies the requirements of veterinary practice.