This study aimed to develop models to predict the color and moisture content of coffee beans cherry harvest state using hyperspectral imaging technique reflectance. Images were acquired from 82 grains cherry coffee arabica variety in ripening fruit, ripe and overripe (50 grains for model calibration and 32 grains for prediction) collected from Andrea, Shigua and Toribio plots in the province Toribio Rodriguez de Mendoza, Amazonas state; the spectral region used is between 400 and 1000 nm. The calibration models were constructed using regression by partial least squares (PLSR, acronym in English) obtaining a coefficient of determination (R2) of 0.95 and root mean square error of cross validation (RMSECV) of 3.30 for the a* value. Subsequently able to predict the color parameter a * cherry coffee beans arabica variety, obtaining a R2 of 0.87 and a root mean square prediction error of 2.0. ...