Background: Probe based detection assays form the mainstay of transcript quantification. Problems with these assays include varying hybridization efficiencies of the probes used for transcript quantification and the expense involved. We examined the ability of a standardized competitive RT-PCR (StaRT PCR) assay to quantify transcripts of 4 cell cycle associated genes (RB, E2F1, CDKN2A and PCNA) in two cell lines (T24 & LD419) and compared its efficacy with the established Taqman real time quantitative RT-PCR assay. We also assessed the sensitivity, reproducibility and consistency of StaRT PCR. StaRT PCR assay is based on the incorporation of competitive templates (CT) in precisely standardized quantities along with the native template (NT) in a PCR reaction. This enables transcript quantification by comparing the NT and CT band intensities at the end of the PCR amplification. The CT serves as an ideal internal control. The transcript numbers are expressed as copies per million transcripts of a control gene such as β-actin (ACTB). Results: The NT and CT were amplified at remarkably similar rates throughout the StaRT PCR amplification cycles, and the coefficient of variation was least (<3.8%) when the NT/CT ratio was kept as close to 1:1 as possible. The variability between the rates of amplification in different tubes subjected to the same StaRT PCR reaction was very low and within the range of experimental noise. Further, StaRT PCR was sensitive enough to detect variations as low as 10% in endogenous actin transcript quantity (p < 0.01 by the paired student's t-test). StaRT PCR correlated well with Taqman real time RT-PCR assay in terms of transcript quantification efficacy (p < 0.01 for all 4 genes by the Spearman Rank correlation method) and the ability to discriminate between cell types and confluence patterns. Conclusion: StaRT PCR is thus a reliable and sensitive technique that can be applied to medium-high throughput quantitative transcript measurement. Further, it correlates well with Taqman real time PCR in terms of quantitative and discriminatory ability. This label-free, inexpensive technique may provide the ability to generate prognostically important molecular signatures unique to individual tumors and may enable identification of novel therapeutic targets.