Hyperspectral imaging comprises the capture of data across hundreds of adjacent, narrowly defined spectral bands, rendering the task of material class identification from these images a complex endeavor. Automated identification of spectral signatures of any object using spectral matching algorithms is a crucial task. There are several spectral matching algorithms available, including, such as Spectral Angle Mapper, Spectral Correlation Angle, Spectral Information Divergence, Jeffries-Matusita distance, Dice Spectral Similarity Coefficient, Kumar-Johnson Spectral Similarity Coefficient, Pearson Correlation Coefficient etc. However, all algorithms have some limitations to overcome this hybrid algorithms introduced. Hybrid approach that combines two or more algorithms has shown better results than using a single algorithm. In hybrid (SIDSAMtan) algorithm we calculate perpendicular distance by taking tangent between spectral information divergence and spectral angle mapper (SID × tan(SAM)), similar way hybridized the Spectral Correlation Angle and Spectral Information Divergence, Pearson Correlation Coefficient and Spectral Angle Mapper, Jeffries-Matusita distance and Spectral Angle Mapper, Kumar-Johnson Spectral Similarity Coefficient and Dice Spectral Similarity Coefficient has been calculated. In this article, we compared the performance of hybrid spectral matching algorithms with applying in both vegetation and mineral spectra. We assessed the power of each hybrid algorithm using the Relative Spectral Discrimination Power (RSDPW) approach and found that SIDSAMtan has better discrimination power than the other hybrid algorithms for both vegetation and mineral spectra.