Acoustic telemetry is a powerful tool for studying fish behavior and survival that relies on the assumption that tag detection reflects the presence of live study subjects. This assumption is violated in cases where tag signals continue to be recorded after predators have consumed tagged study subjects. When such tag predation is possible, it becomes necessary for researchers to diagnose and remove these nonrepresentative detections. Past studies have employed a variety of data filtering techniques to address the issue, ranging from rule-based algorithms that rely on expert judgements of behavior and movement capabilities of study subjects and their predators to automated pattern-recognition techniques using multivariate analyses. We compare four approaches for flagging suspicious tracks or detection events: two rule-based expert-opinion approaches of differing complexity and two unsupervised pattern recognition approaches with and without data from deliberately tagged predators. We compare and illustrate trade-offs between alternative approaches by applying these filtering techniques to a case study on survival estimation of acoustic-tagged juvenile Chinook salmon (Oncorhynchus tshawytscha) in the San Joaquin River, California, United States. Filtering approaches differed in the number and composition of tags suspected of being consumed by predators; the largest differences occurred between the two broad categories, rule-based versus pattern recognition. Despite the appeal of the pattern recognition procedures’ automated nature, those approaches nevertheless required investigator judgement in interpreting results. All methods flagged a small subset (5%) of suspicious tags that had exceptionally long residence times and evidence of upstream transitions; 27% of tags showed evidence of predation based on at least one filter. The complex rule-based filter deemed the most tags suspicious (21%) and the simpler pattern recognition method the fewest (10%). Reach-specific survival estimates from the four filters were mostly within 2% of the unfiltered estimates but differences up to 11% were observed. Sensitivity of survival results to tag predation and predator filtering depends on the study setting, spatiotemporal scale of inference, and habitat use of predators. We recommend that survival studies include clear documentation of filtering methods and report on robustness of results to the filtering approach selected.