The relationship between hunters and their environment is a key component in managing wildlife populations. Identifying hunter's characteristics, motivations, and efforts is crucial to understanding if a hunt will be successful. We predicted that landscape characteristics and moose (Alces alces) densities would affect success of hunts. As in wildlife management programs elsewhere, moose hunters in interior Alaska, USA, must return harvest tickets to the Alaska Department of Fish and Game. These tickets provide location of hunts (Uniform Coding Units) and other details. Our modeling of responses (1997–2001) from harvest tickets indicated that location of hunts, mode of transportation, hunting regulations, use of commercial services, year, density of roads, hunter-to-moose ratio, moose density, and residency of hunters were important predictors of success. In addition, we documented that the linear-regression approach to measuring catch per unit effort (CPUE) was inappropriate because it produced an inverse, but not significant, relationship between hunting effort and success. This outcome occurred because most hunts, particularly for large mammals, ended with the harvesting of an animal. Likewise, modeling of hunter success with logistic regression was similarly biased by measures of hunter effort. We established that a time-to-event Weibull regression provided substantial improvement over standard models of CPUE. Weibull regression accurately represented the positive relationship between effort and success, and it can be used to model length of hunt and other covariates related to hunters and landscape characteristics for predicting success.