Constrained by the single-equation specification, traditional logit or probit models for traffic accident severities can only model one severity at a time. Single-equation models fail to account for the simultaneity of injury severity outcomes in multivehicle collisions. An attempt to account for this simultaneity is made by presenting a simultaneous logit model of endogenous interrelationships among injury severities in multivehicle collisions. Car-truck collision data in Washington State between 1990 and 1996 are used for a simultaneous binary logit model to determine the effects of various exogenous factors in the presence of injury simultaneity, and empirical comparisons with traditional single-equation logit models are also provided. The methodology presented provides for significant efficiency gain when correlation between injury severities is high and can easily be extended to multiple severity categories in multiple-vehicle collisions. Empirically, it is also found that parameter estimates for key roadway design variables may vary greatly between a simultaneous logit and its single-equation counterparts. Another finding from this methodology is that preventive measures for head-on collisions, or enforcement of drunk-driving regulations among truck drivers, as well as limiting the occurrence of high-speed curve designs on truck routes, appear to be the most likely factors for significant potential savings in social costs from car-truck collisions.