Background Potato is the world's third most important food crop, yet cultivar improvement and genomic research in general remain difficult because of the heterozygous and tetraploid nature of its genome. The development of physical map resources that can facilitate genomic analyses in potato has so far been very limited. Here we present the methods of construction and the general statistics of the first two genome-wide BAC physical maps of potato, which were made from the heterozygous diploid clone RH89-039-16 (RH). Results First, a gel electrophoresis-based physical map was made by AFLP fingerprinting of 64478 BAC clones, which were aligned into 4150 contigs with an estimated total length of 1361 Mb. Screening of BAC pools, followed by the KeyMaps in silico anchoring procedure, identified 1725 AFLP markers in the physical map, and 1252 BAC contigs were anchored the ultradense potato genetic map. A second, sequence-tag-based physical map was constructed from 65919 whole genome profiling (WGP) BAC fingerprints and these were aligned into 3601 BAC contigs spanning 1396 Mb. The 39733 BAC clones that overlap between both physical maps provided anchors to 1127 contigs in the WGP physical map, and reduced the number of contigs to around 2800 in each map separately. Both physical maps were 1.64 times longer than the 850 Mb potato genome. Genome heterozygosity and incomplete merging of BAC contigs are two factors that can explain this map inflation. The contig information of both physical maps was united in a single table that describes hybrid potato physical map. Conclusions The AFLP physical map has already been used by the Potato Genome Sequencing Consortium for sequencing 10% of the heterozygous genome of clone RH on a BAC-by-BAC basis. By layering a new WGP physical map on top of the AFLP physical map, a genetically anchored genome-wide framework of 322434 sequence tags has been created. This reference framework can be used for anchoring and ordering of genomic sequences of clone RH (and other potato genotypes), and opens the possibility to finish sequencing of the RH genome in a more efficient way via high throughput next generation approaches.