Artificial insemination (AI) programs play a key role in facilitating rapid genetic and production gains in the sheep industry. However, variable rates of success remain a concern and can be a barrier for adoption. The degree to which various male and female factors influence, and ultimately predict fertility following AI remains unknown, largely due to a lack of large-scale industry data sets for analysis. As such, a preliminary investigation of the effect of several factors which may contribute to the variation in the success of ovine AI was made using data compiled from three separate industry AI programs (N = 3663 ewes). Briefly, sire (n=31), time of AI following progesterone pessary removal (48-57h), uterine tone (scored 1-5) and visual intra-abdominal fat score (scored 1-5) of ewes were recorded at AI and pregnancy rate of each ewe subsequently determined by ultrasound (>50 days post AI). Multivariate regression analysis revealed the likelihood of pregnancy varied substantially with sire (P<0.001), with fertility per sire ranging from 47.2% to 78.3%. AI time post pessary removal, uterine tone and visual intra-abdominal fat score fell short of significance within the multivariate model. These preliminary findings highlight the variability in sheep AI results that can be attributed to sire. Future research involves an industry wide assessment of the aforementioned factors to increase the size of the dataset for analysis and experimental power. Further, advanced in vitro assessment of semen used for AI will be undertaken to understand the reasons behind inter-male variation in fertility and to build a model for the accurate prediction of AI success.