Abstract: Victim identification initiatives undertaken in the wake of Mass Fatality Incidents (MFIs) where high-body fragmentation has been sustained are often dependent on DNA typing technologies to complete their mandate. The success of these endeavors is linked to the choice of DNA typing methods and the bioinformatic tools required to make the necessary associations. Several bioinformatic tools were developed to assist with the identification of the victims of the World Trade Center attacks, one of the most complex incidents to date. This report describes one of these tools, the Mass Disaster Kinship Analysis Program (MDKAP), a pair-wise comparison software designed to handle large numbers of complete or partial Short Tandem Repeats (STR) genotypes, and infer identity of, or biological relationships between tested samples. The software performs all functions required to take full advantage of the information content of processed genotypic data sets from large-scale MFIs, including the collapse of victims data sets, remains re-association, virtual genotype generation through gap-filling, parentage trio searching, and a consistency check of reported/inferred biological relationships within families. Although very few WTC victims were genetically related, the software can detect parentage trios from within a victim’s genotype data set through a nontriangulated approach that screens all possible parentage trios. All software-inferred relationships from WTC data were confirmed by independent statistical analysis. With a 13 STR loci complement, a fortuitous parentage trio (FPT) involving nonrelated individuals was detected. Additional STR loci would be required to reduce the risk of an FPT going undetected in large-scale MFIs involving related individuals among the victims. Kinship analysis has proven successful in this incident but its continued success in larger scale MFIs is contingent on the use of a sufficient number of STR loci to reduce the risk of undetected FPTs, the use of mtDNA and Y-STRs to confirm parentage and of bioinformatics that can support large-scale comparative genotyping schemes capable of detecting parentage trios from within a group of related victims.