Hyperspectral imaging system has been developed to determine the quality of fruits based on the profile mapping of soluble solid content (SSC) in Averrhoa carambola with combining spectral and spatial analysis. The number of samples used is 278 samples. The proposed system consists of a Specim FX-10 Hyperspectral Camera with spectral range 400-1000 nm, workbench slider, two 150 Watt halogen lamps tungsten and a personal computer supported with the software for control the motor speed and hypercube data acquisition. A push-broom technique is applied to acquire hyperspectral images from all sample in the region of 400-1000 nm. The region of interest (ROI) of each sample is obtained at 30×30 pixels. In this research, all of the samples were analyzed using partial least squares (PLS) and principal component analysis (PCA) to obtain prediction models for SSC mapping on star fruit. The best model is then used to create the distribution mapping of the soluble solids content on the star fruit. The performance of the prediction model was evaluated by observing the correlation coefficient, and root means square error. The prediction model of PLS result provided for correlation coefficient value, and root mean square errors value were 0.98, 0.43 and 0.98, 0.48 for prediction model of PCA, respectively. This research showed that hyperspectral imaging system might be useful to predict and to map soluble solid content of star fruit and suitable in an industrial sorting fruit system.