Ethylene propylene diene monomer (EPDM) rubber is widely used in a diverse type of applications, such as the automotive, industrial and construction sectors among others. Due to its appealing features, the consumption of vulcanized EPDM rubber is growing significantly. However, environmental issues are forcing the application of devulcanization processes to facilitate recovery, which has led rubber manufacturers to implement strict quality controls. Consequently, it is important to develop methods for supervising the vulcanizing and recovery processes of such products. This paper deals with the supervision process of EPDM compounds by means of Fourier transform mid-infrared (FT-IR) spectroscopy and suitable multivariate statistical methods. An expedited and nondestructive classification approach was applied to a sufficient number of EPDM samples with different applied processes, that is, with and without application of vulcanizing agents, vulcanized samples, and microwave treated samples. First the FT-IR spectra of the samples is acquired and next it is processed by applying suitable feature extraction methods, i.e., principal component analysis and canonical variate analysis to obtain the latent variables to be used for classifying test EPDM samples. Finally, the k nearest neighbor algorithm was used in the classification stage. Experimental results prove the accuracy of the proposed method and the potential of FT-IR spectroscopy in this area, since the classification accuracy can be as high as 100%.