Agriculture is the art of producing different crop types from the soil and plays an important role in our lives, sustaining and improving the economic sector. This study is mainly focused on spectral discrimination of crop types based on space-borne hyperspectral (PRISMA) sensor over Khanna, Amloh, Bassi Pathanan blocks lies in Punjab state, India. Hyperspectral sensor consists of narrow bands and provide precise, continuous spectral signature which can significantly help to obtain an unambiguous distinction among the crop types. Along with the reflectance and reflectance ratios, these attributes are useful for crop type discrimination using different combinations of metrics and classifiers. PRISMA hyperspectral sensor is used for spectral development and the collected end-member spectra of same crop types at different sites over study area were averaged to produce reference spectra for various specimens. This study evaluates the spectral discrimination between maize, sunflower, moong, sugarcane and chilli. A total of 135’ individual points are surveyed, and each collected field data was accompanied with photo record. Field data collection sites are selected by visual inspection of crop types present in the imagery data covering the study area. The highest spectral reflectance was shown in infrared spectral zone (940 to 1300 nm), relatively low reflectance in the spectral zone (1361 to 1449 nm and 1822 to 1932 nm) while the lowest reflectance was found in the spectral zone (2350 to 2495 nm). The visible region of the crop reflectance spectrum is characterized by low reflectance due to strong absorption by pigments like chlorophyll The implementation of new remote sensing technology in sustainable agriculture can be used more effectively for effective mapping, monitoring, post-harvest productions, minimizing the wastage and simplifying the transportation of output products etc.