The microstructural features present in titanium alloys not only vary over a wide range of length scale but are also highly interdependent. It is not possible to experimentally study the variation effect of only one microstructural feature while maintaining others at a constant value. Due to this complexity, there are no physical models available to relate the microstructural features to the mechanical properties. This limitation can be overcome by using artificial neural networks (ANN) which are capable of establishing highly nonlinear and complex relationship that exists between input and output variables. A variety of architectural and training parameters affect the performance of a neural network. Currently, there are no guidelines available to select an appropriate neural network that can be used to solve a particular class of problem. An attempt is made here to circumvent this problem by using Taguchi based design of experiments (DOE) approach. A total of 8 neural network architectural and training parameters have been identified to study (1) their effect on ANN performance and (2) establish a suitable neural network that can be used to establish the complex relationship that exists between various microstructural features—mechanical properties in a near-α titanium alloy. Results obtained indicate that architectural parameters contribute 58 % while the training parameters contribute 35 % to the network performance and the network so identified can establish the complex non-linear relationship between the microstructural features and mechanical properties. © 2016, The Indian Institute of Metals - IIM.