Background: The development of large genomic resources has become a prerequisite to elucidate the wide-scale evolution of genomes and the molecular basis of complex traits. Linkage maps represent a first level of integration and utilization of such resources and the primary framework for molecular analyses of quantitative traits. Previously published linkage maps have already outlined the main peculiarities of the rainbow trout meiosis and a correspondance between linkage groups and chromosome arms has been recently established using fluorescent in situ hybridization. The number of chromosome arms which were covered by these maps remained unknown. Results: We report an updated linkage map based on segregation analysis of more than nine hundred microsatellite markers in two doubled haploid gynogenetic lines. These markers segregated into 31 linkage groups spanning an approximate total map length of 2750 cM. Centromeres were mapped for all the linkage groups using meiogenetic lines. For each of the 31 linkage groups, the meta or acrocentric structure infered from centromere mapping was identical with those recently found with fluorescent in situ hybridization results. The present map is therefore assumed to cover the 52 chromosome arms which constitute the rainbow trout karyotype. Our data confirm the occurrence of a high interference level in this species. Homeologous regions were identified in eleven linkage groups, reflecting the tetraploid nature of the salmonid genome. The data supported the assumption that gene orders are conserved between duplicated groups and that each group is located on a single chromosome arm. Overall, a high congruence with already published rainbow trout linkage maps was found for both gene syntenies and orders. Conclusion: This new map is likely to cover the whole set of chromosome arms and should provide a useful framework to integrate existing or forthcoming rainbow trout linkage maps and other genomic resources. Since very large numbers of EST containing microsatellite sequences are available in databases, it becomes feasible to construct high-density linkage maps localizing known genes. This will facilitate comparative mapping and, eventually, identification of candidate genes in QTL studies.