Hyperspectralimaging near infrared (HSI-NIR) has the potential to be used as a non-destructive approach for the analysis of new microbiological matrices of agriculture interest. This article describes a new method for accurately and rapidly classifying the etiologicalagents <em>Colletotrichum gossypii</em> (CG) and <em>C. gossypii</em> var. <em>cephalosporioides</em> (CGC) grown in a culture medium, using scattering reflectance HSI-NIR and multivariate pattern recognition analysis. Five strains of CG and 46 strains of CGC were used. CG and CGC strains were grown on Czapek-agar medium at 25 °C under a 12-hour photoperiod for 15 days. Molecular identification was performed as a reference for the CG and CGC classes by polymerase chainreaction of the intergenic spacer region of rDNA. The scattering coefficient <em>µ<sub>s</sub></em> and the absorption coefficient <em>µ<sub>a</sub></em> were obtained, which resulted in a <em>µ<sub>s</sub></em> valuefor CG of 1.37 × 10<sup>19</sup> and for CGC of 5.83 × 10<sup>–11</sup>. These results showed that the use of the standard normal variate was no longer essential and reduced the spectral range from 1000–2500 nmto 1000–1381 nm. The results evidenced two type II errors for the CG 457-2 and CGC 39 samples in the soft independent modelling model of the analogy model. There were no classification errors using the algorithm of the successive projectionsfor variable selection in linear discriminant analysis (SPA-LDA). A parallel validation of the results obtained with SPA-LDA was performed using a box plot analysis with the 11 variables selected by SPA, in which there were no outliers for the HSI-NIR models.The new HSI-NIR and SPA-LDA procedures for the classification of CG and CGC etiological agents are noted for their greater analytical speed, accuracy, simplicity, lower cost and non-destructive nature.