One of our most valuable natural resources is soil. Sustainable agricultural production is achieved with proper soil management. Tillage is considered to be oneof the largest operations, as the most energy need in agricultural production occurs in tillage.The main purpose of this study is to investigate the effects of chisel tine on draftforce and disturbed soil area and estimate them using artificial neural networks(ANN) and multiple linear regression equations (MLR). The experiments werecarried out in a closed soil bin filled with clay loam soil at an average moisturecontent of 13.2% (on dry basis). The draft force and disturbed soil area wereevaluated as affected by the share width at two levels (60 and 120 mm), forwardspeed at four levels (0.7, 1, 1.25 and 1.5 ms-1) and working depth at four levels(160, 200, 240 and 280 mm) at three replications. The draft force varied from0.5 to 1.42 kN, depending on the controlled variables, while the disturbed soilarea varied from 260 to 865 cm2. Test results show that share width, forwardspeed and working depth were significant on the draft force and disturbed soilarea. Input variables of the ANN models were considered share width, forwardspeed and working depth. In prediction of required draft force and disturbed soilarea respectively, on account of statistical performance criteria, the best ANNmodel with coefficient of determination of 0.999 and 0.998, root mean squareerror of 0.010 and 0.016 and mean relative percentage error of 0.960 and 1.673was better performed than the MLR model.