Detecting all classes of genetic variation in livestock species, such as cattle, is a pre-requisite to studying their association to traits of interest. Copy Number Variations (CNVs) are classes of polymorphic DNA regions including deletions, duplications and insertions of DNA fragments of at least 0.5 kb to several Mb, that are copy number variable when compared to a reference genome. CNVs can be identified using various approaches, among those the SNP array data are low cost, dense coverage, and high throughput. The aim of this study was to obtain a consensus genome map of Copy Number Variable Regions (CNVRs) in the Brown Swiss (dataset of 192 bulls), Red Pied Valdostana (dataset of 143 bulls) and Finnish Ayrshire (dataset of 243 bulls) cattle breeds all genotyped on the Illumina Bovine HD BeadChip, and two SNP based CNV calling algorithms. Brown Swiss cattle originated in the Swiss Alps, kept as a triple purpose breed. Once imported in the US, it was mainly selected for increased milk production. The Valdostana Red Pied cattle is the most common autochthonous dual purpose breed in the region Valle d\u2019Aosta in Italy (13,000 animals in 2013, almost all of them registered in the Herd Book). The Finnish Ayrshire is the most common cattle in Finland. CNVs were called with the PennCNV and SVS7 software and were summarized to CNVRs at the population level as overlapping CNV calls within breed. PennCNV identified 2,377, 1,723 and 1,689 for the Italian Brown Swiss, the Red Pied Valdostana and the Finnish Ayrshire, respectively. SVS7 detected 370, 235 and 2,063 for the three cattle breeds. These regions were annotated with Ensembl v78 Bos taurus gene set (UMD3.1) and genomic regions harboring QTL for production and functional traits. The comparison among CNVRs here identified provided common regions in the breeds. The results of this study are a comprehensive genomic analysis of cattle CNVs derived from SNP data, which will be a valuable genomic variation resource and will enrich the bovine CNV map in the cattle genome, providing new information for association studies with traits included in the selection programs