Wild and prescribed fire-induced injury to forest trees can produce immediate or delayed tree mortality but fire-injured trees can also survive. Land managers use logistic regression models that incorporate tree-injury variables to discriminate between fatally injured trees and those that will survive. We used data from 4024 ponderosa pine (Pinus ponderosa Dougl. ex Laws.) and 3804 Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees from 23 fires across Oregon and Washington to assess the discriminatory ability of 21 existing logistic regression models and a polychotomous key (Scott guidelines). We used insights from the validation exercise to build new models for each tree species and to identify fire-injury variables which consistently produce accurate mortality predictions. Only 8% of Ponderosa pine and 14% of Douglas-fir died within 3 years after fire. The amount of crown volume consumed, the number of bole quadrants with dead cambium and the presence of beetles were variables that classified most accurately, but surviving trees in our sample displayed a wide range of fire injury making the accurate classification of dead trees difficult. For ponderosa pine, our new model correctly classified 99% of live trees and 12% of dead trees while the Malheur model (Thies et al., 2006) correctly classified 95% of live trees and 24% of dead trees. The Scott guidelines accurately predicted at least 98% of live ponderosa pine trees but less than 2% of dead ponderosa pine. For Douglas-fir the Scott guidelines accurately predicted at least 80% of live trees and generally less than 10% of dead trees. Misclassification rates can be controlled by the choice of decision criteria used in the models and managers are encouraged to consider costs of the two types of misclassifications when choosing decision criteria for specific land management decisions.