Abstract : This research investigates the application of the discrete wavelet transform (DWT) to the problem of denoising flow signals. The flow signals were made available, for this work, from the records generated by single particle flowing in an experimental system, where each record has 256 data points sampled at a rate of 2000 sps. The measuring technique uses the space-periodic capacitance transducer (SPCT) which generates flow signals with a spectral content which depends on the velocity of the flowing particle above the transducer (SPCT). The work consists of a comparative study among three different methods for signal denoising. Two of them utilize the Fourier transform as their analysis tool, so they can be classified as frequency domain methods. These two methods are spectral subtraction and Wiener filtering. The third method is the wavelet based method which has a large share of the details investigation study in the thesis from the following points of view; the use of the suitable wavelet type for decomposition and reconstruction, the suitable number of levels to be used, the best hresholding method and the corresponding best threshold selection rule. The results of the comparative study are to candidate the wavelet-based denoising method to be the best method as compared with the other two Fourier-based denoising methods. The wavelet comparison procedure that has been followed in the research summarized that; for denoising colored noisy flow signals the best method for thresholding is Garrote (among the six methods studied here) with a best threshold selection rule as a rigursure (among the four rules studied here). For denoising white noisy flow signals the best method for thresholding is Qian with a best threshold selection rule as a minimaxi. Software programs have been implemented to compute the DWT of the SPCT flow signals using MATLAB package (version 6.5) running on a Pentium-IV personal computer.