An artificial-neural-network (ANN) model which is used for the prediction of properties of the as-milled powder is developed for the analysis and prediction of correlations between processing (high-energy planetary ball milling) parameters and the morphological characteristics of Ti2AlNb-based alloy powder by applying the back-propagation (BP) neural network technique.In the BP model, the input parameters of the neural network model are milling speed, milling time and ball-to-powder weight ratio. The output of the model is the properties of the as-milled powder (specifically crystallite size). The number of node in the hidden layer is 9. Input and output functions are tansig and purelin, respectively. The accuracy of the established artificial neural network model was tested by the test data sample. It is shown that the predicted values coincide well with the test results owe to the advantages in fault-tolerance and commonality. Not only can the trained neural network model be used to predict the crystallite size of the as-milled Ti2AlNb-based alloy powder, but also can make up for deficiency of all kinds of physical model for ball milling process in application and expression, which has application value and far-reaching significance for the research work of the actual powder metallurgy process.