This paper presents the validation of a new multispectral camera specifically developed for dermatological application based on healthy participants from five different Skin PhotoTypes (SPT). The multispectral system provides images of the skin reflectance at different spectral bands, coupled with a neural network-based algorithm that reconstructs a hyperspectral cube of cutaneous data from a multispectral image. The flexibility of neural network based algorithm allows reconstruction at different wave ranges. The hyperspectral cube provides both high spectral and spatial information. The study population involves 150 healthy participants. The participants are classified based on their skin phototype according to the Fitzpatrick Scale and population covers five of the six types. The acquisition of a participant is performed at three body locations: two skin areas exposed to the sun (hand, face) and one area non exposed to the sun (lower back) and each is reconstructed at 3 different wave ranges. The validation is performed by comparing data acquired from a commercial spectrophotometer with the reconstructed spectrum obtained from averaging the hyperspectral cube. The comparison is calculated between 430 to 740 nm due to the limit of the spectrophotometer used. The results reveal that the multispectral camera is able to reconstruct hyperspectral cube with a goodness of fit coefficient superior to 0,997 for the average of all SPT for each location. The study reveals that the multispectral camera provides accurate reconstruction of hyperspectral cube which can be used for analysis of skin reflectance spectrum. © 2011 SPIE-OSA.