In recent years the improvements in high-throughput gene expression analysis have led to the discovery of numerous non-protein-coding RNA (npcRNA) molecules. They form an abundant class of untranslated RNAs that have shown to play a crucial role in different biochemical pathways in the cell. Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an efficient tool to measure RNA abundance and gene expression levels in tiny amounts of material. Despite its sensitivity, the lack of appropriate internal controls necessary for accurate data analysis is a limiting factor for its application in npcRNA research. Common internal controls applied are protein-coding reference genes, also termed "housekeeping" genes (HKGs). However, their expression levels reportedly vary among tissues and different experimental conditions. Moreover, application of HKGs as reference in npcRNA expression analyses is questionable, due to the differences in biogenesis. To address the issue of optimal RT-qPCR normalizers in npcRNA analysis, we performed a systematic evaluation of 18 npcRNAs along with four common HKGs in 20 different human tissues. To determine the most suitable internal control with least expression variance, four evaluation strategies, geNORM, NormFinder, BestKeeper, and the comparative delta C(q) method, were applied. Our data strongly suggest that five npcRNAs, which we term housekeeping RNAs (HKRs), exhibit significantly better constitutive expression levels in 20 different human tissues than common HKGs. Determined HKRs are ideal candidates for RT-qPCR data normalization in human transcriptome analysis, and might also be used as reference genes irrespective of the nature of the genes under investigation.