Rapid and reliable animal fur identification has remained a challenge for customs inspection. The accurate distinction between fur types has a significant meaning in implementing the correct tariff policy. A variety of analytical methods have been applied to work on distinguishing animal fur types, with tools of microscopy, molecular testing, mass spectrometry, Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. In this research, the capability of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with pattern recognition methods was investigated for the discrimination of animal fur in six types. This work was to explore the non-destructive application of ATR-FTIR technique in discriminant analysis of animal fur. All spectra were collected by ATR-FTIR of the wavenumber ranging from 4000 to 650 cm. Data pretreatments included moving average smoothing and multiplicative scatter correction (MSC). Four supervised classification algorithms were chosen to categorize the types of fur: soft independent modeling of class analogy (SIMCA), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM). PLS-DA and LS-SVM were both effective approaches, with a 100% classification accuracy rate. The accuracy of PCA-LDA and SIMCA was 98.33% and 99.44%, respectively. Furthermore, LS-SVM model obtained using Monte-Carlo sampling method also obtained 100% prediction accuracy, while all other methods produced misclassification. LS-SVM corrected the non-linearities for the animal fur FTIR data but also remarkably improved the prediction performance level. The results of this study revealed that the combination of ATR-FTIR and chemometrics has a huge potential for animal fur discrimination.