1. Genotyping errors are rules rather than exceptions in reality, and are found in virtually all but very small datasets. These errors, even when occurring at an extremely low rate, can derail many genetic analyses such as parentage/sibship assignments and linkage/association studies. 2. Nonetheless, few robust and accurate methods are available for estimating the rate of occurrence of genotyping errors and for identifying individual erroneous genotypes at a locus. Methods based on duplicate genotyping are expensive, and estimate genotype inconsistency rather than error rate at a locus. Methods based on Hardy-Weinberg equilibrium tests have low robustness and low power, and apply only to those particular errors that cause excessive homozygosity. Methods based on pedigrees are powerful, robust and accurate. However, they rely on known and complete pedigrees that are unfortunately rarely available from natural populations in the wild. 3. I proposed a maximum likelihood method to reconstruct pedigrees from genotype data with errors occurring at a roughly estimated (presumed) rate. In this paper, I describe how to use the method and inferred pedigree in estimating allelic dropout (or null allele) rate and false allele rate jointly at each marker locus, in identifying the erroneous genotypes, and in inferring the most likely genotypes at each locus of each individual. I examine the power, accuracy and robustness of the method by extensive simulations, and demonstrate the usefulness of the method by analysing three empirical datasets. 4. It is concluded that, both pedigrees and the rates of genotyping errors at each locus can be reliably estimated from the same genotype data by the same likelihood method, when marker information is sufficient and some sampled individuals are first-degree relatives. The erroneous genotypes are however inferred conservatively, and are reliably detected only when they occur in large families and/or at highly polymorphic loci. Estimation of genotyping error rates per locus and identification of erroneous genotypes of each individual at each locus should be routinely conducted to assess and improve data quality, to highlight markers for optimization of genotyping protocols or for replacement, and to enable the integration of genotyping errors in a robust statistical analysis.