Copy number abnormalities (CNAs) such as somatically-acquired chromosomal deletions and duplications drive the development of cancer. As individual tumor genomes can contain tens or even hundreds of large and/or focal CNAs, a major difficulty is differentiating between important, recurrent pathogenic changes and benign changes unrelated to the subjects phenotype. Here we present Copy Number Explorer, an interactive tool for mining large copy number datasets. Copy Number Explorer facilitates rapid visual and statistical identification of recurrent regions of gain or loss, identifies the genes most likely to drive CNA formation using the cghMCR method and identifies recurrently broken genes that may be disrupted or fused. The software also allows users to identify recurrent CNA regions that may be associated with differential survival.