In the industry, the pneumatic conveying of wheat grains has attracted a lot of attention due to benefits such as enclosed transfer and flexibility in routing. Measuring the mass flow of wheat grains is important to avoid problems such as wear of the transmission pipes, pipeline clog due to dense phase and fracture of the seeds. In this research, the acoustic signal analysis method was used to detect the mass flow rate of wheat grains at three levels of 1.5, 3 and 4.5 kg/min in the pneumatic conveying. The signals decomposition was done in time-frequency domain (wavelet transform) with 9 levels. The properties of Sum, Mean, Variance (VAR), Root Mean Square (RMS), Skewness, Kurtosis, and Moment were compared. The results showed that the first (d(1)), second (d(2)), fifth (d(5)), sixth (d(6)) and seventh (d(7)) detail sub-signals have the highest ability and priority to detect mass flow levels, respectively. Also among the studied properties, sum, mean, VAR, RMS, and moment, are prioritized for detecting mass flow levels with probability level of 1%, respectively. The values of all these properties increased with increasing mass flow rate. The acoustic signal analysis technique has a good potential for detecting the different mass flow levels of conveyed wheat grains.