Background: Costs allocation methods are important for economic evaluation of health care. Aim: To evaluate the impact of overhead costs rates of different hospitals on the cost-effectiveness rankings of health programs. Material and Methods: Using the cost reports from eight hospitals, a Montecarlo simulation was implemented, programming the complete micro-costing algorithm to calculate the final cost of 47 health care interventions, from the health sector perspective. The independent variables considered were the overhead cost rates per establishment and the actual overhead costs. Changing these variables, resulted in changes of the final cost of interventions and cost- effectiveness ratios. Finally the probabilities of changes in the cost-effectiveness ranking of each intervention were calculated. Results: Thirteen programs did not change their ranking order. However, 34 interventions modified their position with different occurrence probabilities. In the new proposed ranking, 21 programs changed their position from one to six places. Conclusions: Different overhead cost rates, representing different assignation forms, have a relative impact in the cost-effectiveness order. Montecarlo simulation can help to improve the accuracy of ranking assignment.Background: Costs allocation methods are important for economic evaluation of health care. Aim: To evaluate the impact of overhead costs rates of different hospitals on the cost-effectiveness rankings of health programs. Material and Methods: Using the cost reports from eight hospitals, a Montecarlo simulation was implemented, programming the complete micro-costing algorithm to calculate the final cost of 47 health care interventions, from the health sector perspective. The independent variables considered were the overhead cost rates per establishment and the actual overhead costs. Changing these variables, resulted in changes of the final cost of interventions and cost- effectiveness ratios. Finally the probabilities of changes in the cost-effectiveness ranking of each intervention were calculated. Results: Thirteen programs did not change their ranking order. However, 34 interventions modified their position with different occurrence probabilities. In the new proposed ranking, 21 programs changed their position from one to six places. Conclusions: Different overhead cost rates, representing different assignation forms, have a relative impact in the cost-effectiveness order. Montecarlo simulation can help to improve the accuracy of ranking assignment.