SDT is a free, user-friendly computer program designed for the objective classification of viral genome sequences based on pairwise sequence alignment and identity calculations. It provides publication-quality pairwise identity plots and color-coded distance matrices to aid in classifying sequences according to ICTV-approved taxonomic demarcation criteria. SDT is available for Windows, Linux, and macOS, with a parallel version for high-performance computing clusters. The program uses pairwise sequence alignment methods, such as those implemented in MUSCLE, ClustalW, or MAFFT, to calculate identity scores, ignoring alignment positions containing indel characters. It generates a frequency distribution of pairwise identities and a color-coded matrix to visualize sequence relationships. SDT is particularly useful for virologists seeking to classify new viral genome sequences into established ICTV-approved species, strains, or other operational taxonomic units (OTUs). It is more consistent than other tools like PASC and DEmARC, which rely on multiple sequence alignments and may produce inconsistent results. SDT's parallel version significantly improves analysis speed, making it suitable for large datasets. The program is also useful for creating datasets based on sequence identities and for analyzing the diversity of viral genomes. SDT is a complementary tool that can be used alongside other methods to establish and implement pairwise identity-based virus classification systems. It is freely available at www.cbio.uct.ac.za/SDT.SDT is a free, user-friendly computer program designed for the objective classification of viral genome sequences based on pairwise sequence alignment and identity calculations. It provides publication-quality pairwise identity plots and color-coded distance matrices to aid in classifying sequences according to ICTV-approved taxonomic demarcation criteria. SDT is available for Windows, Linux, and macOS, with a parallel version for high-performance computing clusters. The program uses pairwise sequence alignment methods, such as those implemented in MUSCLE, ClustalW, or MAFFT, to calculate identity scores, ignoring alignment positions containing indel characters. It generates a frequency distribution of pairwise identities and a color-coded matrix to visualize sequence relationships. SDT is particularly useful for virologists seeking to classify new viral genome sequences into established ICTV-approved species, strains, or other operational taxonomic units (OTUs). It is more consistent than other tools like PASC and DEmARC, which rely on multiple sequence alignments and may produce inconsistent results. SDT's parallel version significantly improves analysis speed, making it suitable for large datasets. The program is also useful for creating datasets based on sequence identities and for analyzing the diversity of viral genomes. SDT is a complementary tool that can be used alongside other methods to establish and implement pairwise identity-based virus classification systems. It is freely available at www.cbio.uct.ac.za/SDT.