One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break(UB: unplanned dilution and ore-loss). Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study,the contribution of ten major UB causative parameters has been scrutinised based on a published UB predicting artificial neuron network(ANN) model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, i.e., connection weight algorithm(CWA) and profile method(PM) have been applied. As a result of CWA and PM applications, adjusted Q rate(AQ) revealed as the most influential parameter to UB with contribution of 22.40% in CWA and 20.48% in PM respectively. The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine. One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break (UB: unplanned dilution and ore-loss). Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study, the contribution of ten major UB causative parameters has been scrutinised based on a published UB predicting artificial neuron network (ANN) model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, ie, connection weight As a result of CWA and PM applications, adjusted Q rate (AQ) revealed as the most influential parameter to UB with contribution of 22.40% in CWA and 20.48% in PM respectively The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine.