Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (Odobenus rosmarus divergens) and fishers (Pekania pennanti). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.