The algorithm we have developed successfully provided pathogen identification to the maximum level of detail possible (species or strain) depending on the quality of each Pro-Seq. This identification capability requires minimal input on the identity of the pathogens, making non-expert use feasible. The crucial feature incorporated that allowed complete automation was the use of taxonomic databases, which classify organisms into ordered groups and provide relationships between organism entries, allowing removal of redundancies, comparison of different related prototype sequences and simplification of data presentation. This allows databases, i.e. NCBI, that are redundant and subject to minimal curation but which constantly receive updated and new sequence information to be used with great success. Although we have demonstrated this using only the NCBI databases, other databases or custom made ones could have easily been used, which might improve performance. The algorithm is capable of providing accurate identifications at all analysis levels for pathogens that are less variable or are represented by highly conserved ProSeqs. For more variable or rapidly mutating pathogens, e.g. Influenza A virus, ProSeq identification Task(I) and ProSeq grouping Task(II) still provided accurate detailed identifications, but the pathogen determination Task(III) was unable to report fine scale discrimination. The comparison of the conventionally sequenced Influenza virus gene sequences illustrated that the algorithm is capable of automatically adjusting for updates in databases. The algorithm demonstrated its capability to properly distinguish hybridizations on a ProSeq caused by the specified pathogen from those caused by genetically close (near neighbor) strains and did not make incorrect identifications, eliminating one potential cause of false positives. Filtering the raw hybridization results served to reduce the computation time, accounted for potential primer interference and more importantly reduced potential biasing. This simple integrated algorithm provided sufficient and accurate identification, so that immediate use of the RPM v.1 or similar resequencing arrays and assay is possible. Although not discussed in this paper, the algorithm has successfully detected the presence of simulated multi-infections (Lin et al., submitted for publication). The algorithm as currently developed will detect mixtures when the organisms have sufficient variation; however, detection of a mixture of an organism and its mutation strain in a sample is uncertain in its present phase. In principle it may be possible to detect such mixtures as the resequencing microarray can detect and sequence diploid organisms.