The Pacific abalone ( Haliotis discus hannai ) is a commercially important abalone species native to South Korea, which is immensely popular as a seafood delicacy. Selective breeding programs have been implemented to ensure the production of high-quality abalone, resulting in genetic improvement within domestic populations. With recent advances in statistical and scientific methodologies, there has been a shift toward genome-based selective breeding methods and away from traditional phenotype- or pedigree-based approaches. To improve the accuracy of genome-based selection, it is necessary to obtain genomic data from as many individuals as possible. Single nucleotide polymorphism (SNP) markers are commonly used genetic markers, which can be analyzed using SNP arrays. Genetic variation can be detected efficiently using SNP arrays, which are semiconductor chips carrying information on tens of thousands of genetic variants. We have developed and validated an abalone SNP array carrying 65,740 variants (60 K abalone SNP array). Utilizing whole-genome sequence data from 29 abalones, the SNP array was designed by extracting and filtering variants based on a number of criteria, including minor allele frequency > 0.2, genotype missing ratio > 0.9, Hardy-Weinberg Equilibrium P < 0.0001, SNP even spacing, and final score > 0.9. Validation experiments demonstrated good performance of the designed SNP array with a call rate of 90.47 %, polymorphic SNPs of 76.03 %, and linkage disequilibrium correlation with whole-genome sequence data of 0.991. This 60 K SNP array is suitable for use in constructing genomic databases and will be beneficial for genome-based selective abalone breeding programs. • Custom 60 K SNP array designed from 29 domestic abalone genomes. • Array validation shows 90.47 % call rate, 76.03 % polymorphic SNPs in 1892 abalone. • High linkage disequilibrium (0.991) with whole genome sequence data indicates reliability. • Enables efficient large-scale genotyping for genome-based abalone breeding. • Future updates aim to improve monomorphic trait SNPs with larger samples.