The red edge parameters of plants spectrum were used to estimate foliar chlorophyll for nitrogen content and leaf area. Among these parameters, the red edge position (REP) is the best one for diagnosing the growth state of tomato according to statistical analysis. The REP was defined by the wavelength of the maximum first derivative of the reflectance spectrum in the region (660nm to 780nm) of the red edge. The six algorithms could be used to extract the REP, including fourpoint interpolation, maximum first derivative, inverted Gaussian fitting, Lagrangian, linear extrapolation, and polynomial fitting. In order to achieve a rapid and accurate application for predicting the chlorophyll content of tomato with REP, this study systematically analyzed the quantitative relationships and statistical characters between REP on various algorithms and leaf chlorophyll status, and then the linear regression, logarithmic regression, power regression, exponential regression and quadratic polynomial regression were used to develop the prediction models of the chlorophyll content for each REP extraction algorithm. The result showed that the logarithmic model of the linear extrapolation had the best accuracy and reliability. The calibration R2c was 0.6186, the validation R2v was 0.7711 and the root mean squared error of validation set (RMSv) was 8.3596. The exponential model of the fourpoint interpolation could be obtained easily according to reflectance at 670nm, 700nm, 740nm and 780nm, the calibration R2c was 0.6217, validation R2v was 0.7666 and RMSEv was 8.5682. The predictive ability was good enough to develop a monitoring instrument of tomato chlorophyll content. 