Bacterial identification methods used in routine identification of pathogens in medical microbiology include a combination approach of biochemical tests, mass spectrometry or molecular biology techniques. Extensive publicly-available databases of DNA sequence data from pathogenic bacteria have been amassed in recent years; this provides an opportunity for using bacterial genome sequencing for identification purposes. Whole genome sequencing is increasing in popularity, although at present it remains a relatively expensive approach to bacterial identification and typing. Complexity-reduced bacterial genome sequencing provides an alternative. We evaluate genomic complexity-reduction using restriction enzymes and sequencing to identify bacterial isolates. A total of 165 bacterial isolates from hospital patients in the Australian Capital Territory, between 2013 and 2015 were used in this study. They were identified and typed by the Microbiology Department of Canberra Public Hospital, and represented 14 bacterial species. DNA extractions from these samples were processed using a combination of the restriction enzymes PstI with MseI, PstI with HpaII and MseI with HpaII. The resulting sequences (length 30–69 bp) were aligned against publicly available bacterial genome and plasmid sequences. Results of the alignment were processed using a bioinformatics pipeline developed for this project, Currito3.1 DNA Fragment Analysis Software. All 165 samples were correctly identified to genus and species by each of the three combinations of restriction enzymes. A further 35 samples typed to the level of strain identified and compared for consistency with MLST typing data and in silico MLST data derived from the nearest sequenced candidate reference. The high level of agreement between bacterial identification using complexity-reduced genome sequencing and standard hospital identifications indicating that this new approach is a viable alternative for identification of bacterial isolates derived from pathology specimens. The effectiveness of species identification and in particular, strain typing, depends on access to a comprehensive and taxonomically accurate bacterial genome sequence database containing relevant bacterial species and strains.