This study was aimed to determine the performance of linear discriminant analysis (LDA) for differentiation between seasons of calving on the basis of 305day- milk yield, fat %, protein %, days open (DO), days to first insemination (DFI), and number of services per conception. By considering the assumption of this method, a random sample was selected from the animals being represented by all explanatory variables. The discrimination between seasons of calving was depended on the significance of coefficients, classification rate, in addition to the group centroids. Results showed that LDA method selected 305day-milk yield (kg), days open (DO), days to first insemination and number of services per conception, as the significant (P < 0.05) contributors for data classification. The total variance explained by 2 functions was 79.2% and 15.9%, respectively. So, the 1st function can do well in discrimination process than the 2ndone. The percentage of correct classification was 64.6%. In conclusion, LDA can be used effectively for classification of calving seasons, even with violation of normality assumption