For water glycol fire-resistant hydraulic fluids (HFC), the commonly used methods for measuring the moisture content and viscosity present problems such as complicated operation, long time-consuming, high measurement cost, etc. The application of mid-infrared spectroscopy to HFC moisture content and viscosity measurement was studied. The sample set consisted of 85 actual in-use oil samples. The moisture content and kinematic viscosity were measured by traditional coulometric method and capillary method. The mid-infrared spectra of samples was collected using an ATR liquid pool. Spectra pretreatments such as spectra correction, background subtraction, Savitzky-Golay (SG) smoothing and baseline correction were performed. During the process of constructing the moisture content measurement model, strong influence points were searched by student residuals-leverage method, which were determined as abnormal samples and removed. Mahalanobis distance SPXY method was used to divide the sample set into modeled samples and verification samples. According to the Beer-Lambert law, the moisture content was directly proportional to the infrared absorbance. So a linear method should be used to construct the relationship between the moisture content and the infrared absorbance. At the same time, the no information variables, redundant information and noise in the spectrum need to be eliminated to improve the model robustness and generalization ability. By the backward interval partial least squares method (BiPLS), the moisture content measurement feature bands were optimized, and a linear calibration analysis model was established. The results showed that the minimum root mean square error of cross validation (RMSECV) was obtained when the bands of 3 526~3 354, 1 790~1 618, 3 005~2 660 and 1 096~924 cm-1 remained. The preferred feature bands can be explained as that, and the OH amount in moisture was calculated by subtracting the OH amount in ethylene glycol converted by the 3 005~2 660 and 1 096~924 cm-1 bands from the all OH amount in the HFC system converted by the 3 526~3 354 cm-1 band, and then the moisture content was calculated. In order to improve the accuracy, the 1 790~1 618 cm-1 band, corresponding to the moisture OH swing vibration absorption peak, was used as an auxiliary wave for measuring the moisture content. The independent verification samples were used to test the model. The results showed that the correlation coefficient (r) of the established linear model was 0.989 5 and the root mean square error of prediction (RMSEP) was 0.405 2, which met the accuracy requirement for moisture content measurement in HFC. During the process of constructing the viscosity measurement model, the outlier samples were searched by Mahalanobis distance method, which were determined as abnormal samples and removed. Mahalanobis distance SPXY method was used to divide the sample set into modeled samples and verification samples. Viscosity was a physicochemical index that had complex relationships with multiple factors, and was nonlinear to the infrared spectrum. Based on principal component analysis combined with BP neural network method (PCA-BPNN), a nonlinear viscosity correction analysis model was established. The first 10 principal components with a cumulative contribution rate of 95.12% were extracted as input, and the measured viscosity values were treated as output. A single hidden layer BP neural network was created and trained. The training result correlation coefficient r was 0.996 8. The independent verification samples were used to test the model. The results showed that the r of the established nonlinear model was 0.984 3 and the RMSEP was 0.615 1, which met the accuracy requirement for HFC viscosity measurement and were superior to the ones obtained by the BiPLS linear model. Four mid-infrared bands were identified and can be used for moisture content measurement in HFC, which could provide basis for narrowband infrared spectroscopy or other similar studies. The research results showed that the mid-infrared spectroscopy combined with BiPLS and PCA-BPNN analysis method can be applied to the measurement of moisture content and viscosity of HFC, and the accuracy meets the monitoring requirements. Compared with the traditional measurement methods, a new test method is provided with the features of being fast, non-destructive and low-cost.