To explore a rapid method for detecting moisture content of cow’s milk, a network analyzer and an openended coaxialline probe were applied to measure the dielectric properties (relative dielectric constant and dielectric loss factor) of 105 milk samples over the frequency range of 20~4500MHz at room temperature (25±0.5)℃. The low linear correlation coefficient between the moisture content and the permittivities at a single frequency of used milk samples showed that it was difficult to predict the moisture content of milk using a single permittivity value. Therefore, the dielectric spectra combined with chemometrics were used to determine the moisture content of milk. All samples were partitioned into calibration set (75 samples) and prediction set (30 samples) by using set partitioning method based on joint X-Y distances. Fifteen characteristic variables that predicting moisture content of cow’s milk were selected by successive projection algorithm from full spectra. The generalized regression neural network, support vector machine and extreme learning machine models were established to predict moisture content of milk (87.28%~91.30%), based on the original full dielectric spectra and characteristic variables. The results showed that the extreme learning machine model established using the characteristic variables selected by successive projection algorithm was the best model in determining moisture content of milk, with the correlation coefficient of prediction, rootmeansquare error of prediction, and residual prediction deviation of 0.988, 0.119%, and 6.723, respectively. The study indicates that the dielectric spectra combined with chemometrics could be used to detect moisture content of milk. The research is helpful to develop a new milk moisture detector which could be used in situ or online detection.