Tag-return models can be used to estimate survival rates and tag recovery rates. The additional knowledge of an estimated tag reporting rate allows one to separate total mortality into fishing and natural mortality rates. This paper examines the use of hi.-h-reward tags in tagging studies. We find that many of the problems encountered in tagging studies can be avoided if tagged animals are released in small batches in as many locations as possible rather than in large batches at a few locations. Often, the use of substantial monetary rewards for the return of standard tags may be justified as cost effective because of the higher tag return rates they induce. The high-reward tagging method is an important method for estimating the tag reporting rate for standard tags. For this method it is assumed that high-reward tags are reported 100% of the time. This assumption is investigated. Other assumptions of the method are also considered, and particular attention is paid to whether the reporting rate of standard tags may change when a high-reward tagging study is initiated. This is of particular concern in cases in which standard tags are used for all study years and high-reward tags are only used in some subset of the study years. If the natural mortality rate is assumed to be constant over all years, then fishing and natural mortality together with two tag reporting rates can be estimated. Simulation analysis shows that fishing mortality estimates are unbiased in this case but have significantly higher coefficients of variation in the years without high-reward tags. Natural mortality estimates are unbiased and reasonably efficient, but this is crucially dependent on the assumption that natural mortality is constant over time. We make detailed recommendations for improving the design of reward tagging studies in general.