The development of computational tools that permit microstructurally-based predictions for tensile and fracture toughness properties of commercially important titanium alloys is a valuable step towards the accelerated maturation of materials. This talk will discuss the development of Neural Network Models based on Bayesian statistics to predict the yield strength, ultimate tensile strength and toughness of Ti-6Al-4V at room temperature. The development of such rules-based models requires the population of extensive databases which contain both compositional and microstructural information. These databases have been used to train and test Neural Network models to predict the tensile and toughness properties. In addition, these models have been successfully used to identify the influence of individual microstructural features on the mechanical properties, consequently guiding the efforts towards development of more robust phenomenological models. The influence of the individual microstructural features on tensile and toughness will be discussed.