In this study performed experimental and theoretical analysis of mouldability for feedstocks used powder injection moulding. This study covers two main subjects: (i) The experimental analysis: The barrel temperature, injection pressure and flow rate are factors for powder injection moulding (PIM). Powder-binder mixture using as feedstock as feedstock in PIM requires a little more attention and sensitivity. Obtaining the balance among with pressure, temperature and especially flow rate are the most important in aspect of undesirable conclusions such as powder-binder separation, sink marks and crack in moulded party structure. In this study; available feed stocks using in PIM were injected in three different cavities which consist of zigzag form, constant cross section and stair form (in five different thicknesses) mouldability them are measured. Because of difference between material and binder measured lengths were different. These were measured as 533 mm, 268 mm, 211 mm and 150 mm in advanced materials trade mark Fe-2Ni, BASF firm Catamould AO-F, FN02, 316L stainless steel, respectively. (ii) The theoretical analysis: The use of artificial neural network (ANN) has been proposed to determine the mouldability for feedstocks using in powder injection moulding using results of experimental analysis. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with three and four neurons in the hidden-layer, which made it possible to predict yield length with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R 2 values are 0.999463, 0.999445, 0.999574 and 0.999593for Fe-2Ni, BASF firm Catamould AO-F, FN02, 316L stainless steel, respectively. Similarly, these values for testing data are 0.999129, 0.999666, 0.998612 and 0.997512, respectively. As seen from the results of mathematical modeling, the calculated yield length are obviously within acceptable uncertainties. (C) 2008 Elsevier Ltd. All rights reserved.