In the investigation of the expression levels of target genes, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is the most accurate and widely used method. However, a normalization step is a prerequisite to obtain accurate quantification results from RT-qPCR data. Therefore, many studies regarding the selection of reference genes have been carried out. Recently, these studies have involved large-scale gene analysis methods such as microarray and next generation sequencing. In our previous studies, we analyzed large amounts of transcriptome data from the cynomolgus monkey. Using a modification of this large-scale transcriptome sequencing dataset, we selected and compared 12 novel candidate reference genes (ARFGAP2, ARL1, BMI1, CASC3, DDX3X, MRFAP1, ORMDL1, RSL24D1, SAR1A, USP22, ZC3H11A, and ZRANB2) and 4 traditionally used reference genes (ACTB, GAPDH, RPS19, and YWHAZ) in 13 different whole-body tissues by the 3 well-known programs geNorm, NormFinder, and BestKeeper. Combined analysis by these 3 programs showed that ADP-ribosylation factor GTPase activating protein 2 (ARFGAP2), morf4 family associated protein 1 (MRFAP1), and ADP-ribosylation factor-like 1 (ARL1) are the most appropriate reference genes for accurate normalization. Interestingly, 4 traditionally used reference genes were the least stably expressed in this study. For this reason, selection of appropriate reference genes is vitally important, and large-scale analysis is a good method for finding new candidate reference genes. Our results could provide reliable reference gene lists for future studies on the expression of various target genes in the cynomolgus monkey.