The dairy industry is a significant source of employment in Kenya. With a suitable policy framework, for example, subsidies and economic incentives coupled with suitable TD models, the level of milk recorded will increase, which is crucial for any meaningful accurate genetic evaluation. Increased genetic gain and improved profits can accrue from using TDMY on dairy heifer at early stage of lactation, unproductive cows would be culled early, and there will be decreased generation interval. In Kenya, we are yet to embrace the use of TD observations instead of aggregated 305-day production records despite several studies having shown advantages (Ilatsia et al. 2006; Mostert et al. 2006). If adopted, the country stand to gain in that ranking of animals could change significantly as observed in other countries (Schaeffer et al. 2000). Breeding programs in Kenya are based primarily on milk production, and therefore, accurate measurement or prediction of milk yield is essential for proving bull faster and eventually high genetic gain. Dairy yield prediction is a current challenge, which has been improved using different statistical methods. Recently, artificial neural networks (ANNs) have been employed as an alternative method of milk yield prediction.