Abstract: Good WC-30Co/45 steel TIG(tungsten inert gas) welded joint could be obtained using Ni-Fe-C alloy as filler metal.However,Ni-Fe-C filler metal was usually developed with the "trial-and-error" method,which wasted a lot of time and efforts.A model was developed for analysis and prediction of correlation between input parameters(welding parameter and content of filler metal) and bend strength in WC-30Co/Ni-Fe-C/45 steel TIG welding process using artificial neural network(ANN).The model was based on multiplayer back propagation neural network and trained with data sets from experiments followed by data normalization.Mean-squared-error of this model was analyzed.The bend strength was further predicted using the trained ANN model.The results showed that when joints welded with filler metals contaning(0.6wt.%) or 0.8wt.% C,and Ni/Fe ratio in the range from 1.9 to 2.7,were obtained,and higher bend strength could be reached.The ANN model could be well used to estimate the effect of parameters on bend strength of WC-30Co/Ni-Fe-C/45 steel TIG welded joints,superior to conventional techniques.