Titanium matrix composites (TMCs) have wide application prospects in the field of aerospace, automobile and other industries because of their good properties, such as high specific strength, good ductility, and excellent fatigue properties. However, in order to improve their fatigue strength and life, crack initiation and growth at the surface layers must be suppressed using surface treatments. Shot peening (SP) is an effective surface mechanical treatment method widely used in industry which can improve the mechanical properties of a surface. However, artificial neural networks (ANNs) have been used as an efficient approach to predict and optimize the science and engineering problems. In the present study the effects of SP on TMC were modeled by means of ANN and the capability of the ANN in predicting the output parameters is investigated. A back-propagation (BP) error algorithm is developed for the network training. Data of experimental tests on the (TiB + TiC)/Ti-6Al-4V composite are employed in order to train the network. The volume fractions of the reinforcements (TiB + TiC) were 5 % and 8 %. ANN testing is accomplished using different experimental data thaat were not used during the network training. The distance from the surface (depth) and SP intensity are regarded as input parameters and residual stress and hardness of the Ti-6Al-4V before and after the SP and adding reinforcements are gathered as the output parameters of the network. A comparison was made between experimental and predicted data. The predicted results were in good agreement with experimental ones, which indicates that developed neural network can be used for modeling the SP process on TMCs. Keywords: titanium matrix composites, surface treatment, shot peening, artificial neural networks, residual stress, hardness Kompoziti na osnovi titana (TMCs) imajo {iroko mo`nost uporabe na podro~ju letalstva, avtomobilske in druge industrije zaradi njihovih dobrih lastnosti, kot so: velika specifi~na trdnost, dobra duktilnost in odli~na odpornost na utrujanje. Vseeno pa je za pove~anje odpornosti na utrujanje in`ivljenjsko dobo, potrebna povr{inska obdelava, da se zavre nastanek razpok in njihova rast na povr{ini. Hladno povr{insko kovanje (SP) je u~inkovita mehanska metoda, ki se v industriji pogosto uporablja za izbolj{anje mehanskih lastnosti povr{ine. Umetne nevronske mre`e (ANNs) se uporabljajo kot u~inkovit pribli`ek za napovedovanje in optimiranje znanstvenih osnov in in`eniringa tega problema. V {tudiji so bili modelirani vplivi SP na TMC s pomo~jo ANN in preiskovana je bila zmo`nost napovedovanja izhodnih parametrov z ANN. Za usposabljanje mre`e je bil razvit algoritem vzvratnega {irjenja napak (BP). Podatki iz eksperimentalnih preizkusov na (TiB + TiC)/Ti-6Al-4V kompozitu so uporabljeni za usposabljanje mre`e. Volumska dele`a delcev (TiB + TiC) za oja~anje sta bila 5 % in 8 %. ANN preizku{anje je bilo izvedeno z uporabo razli~nih eksperimentalnih podatkov, ki niso bili uporabljeni pri usposabljanju mre`e. Razdalja od povr{ine (globina) in...