The development of a set of computational tools that permit microstructurally-based predictions for the tensile properties of commercially important titanium alloys, such as Ti-6Al-4V, is a valuable step towards the accelerated maturation of materials. This paper will discuss the development of Fuzzy Logic and Neural Network Models based on Bayesian statistics to predict the yield strength, ultimate tensile strength and elongation of Ti-6Al-4V at room temperature. The development of such rules-based models requires the building up of extensive databases, which in the present case are microstructurally-based. The steps involved in database development include controlled variations of the microstructure using novel combinatorial approaches to heat-treatments, the use of standardized stereology protocols to rapidly characterize and quantify microstructural features, and mechanical testing of the heat-treated specimens. These databases have been used to train and test the Fuzzy Logic and Neural Network models to predict the tensile 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.