Near-infrared transmittance spectroscopy was used to identify brake fluid brands, new and used brake fluid of each brand. The transmittance spectra of the new and used samples of 4 different brands of brake fluid, including BMW, Toyota, Volvo and Castrol were collected. PCA was conducted to the spectral data of the new samples of the four brake fluid and the spectral data of the new and used samples of each brand. The PCA scores scatter plot indicated that there were differences among the four brands of brake fluid, and there were also differences between new and used samples of each brand. Optimal wavelengths were selected for identifying different brands and new and used samples of each brand by loadings of PCA. Classification models were built using the optimal wavelength, including Partial least squares-discriminant analysis (PLS-DA), Linear discriminant analysis (LDA), Soft independent modeling of class analogy (SIMCA), k-nearest neighbor algorithm (KNN), Random forest (RF), Back propagation neural network (BPNN), Radial basis function neural network (RBFNN), Extreme learning machine (ELM), Support vector machine (SVM), Least-squares support vector machine (LS-SVM). All classification models obtained good performances, the classification accuracy of the calibration set and the prediction set are 100% for most models. Compared with other three brands, new and used samples of Castrol showed slighter difference, and KNN and LS-SVM models performed worse with classification accuracy under 100% in the calibration set. The overall results indicated that near-infrared transmittance combined with optimal wavenumber selection and classification methods could be used to identify brake fluid brands, new and used brake fluids, the results of this study could provide theoretical support for developing online and portable devices.