The thesis investigates the application of the wavelet transform (WT) to the problem of measuring the velocity of solids flowing in pneumatic conveyors. The technique is based . on the use of a space-periodic capacitance transducer (SPCT) which generates flow signals with a spectral content dependent on the velocity of the flowing particles. The wavelet transform maps the signals into a plane called the time-scale plane. It uses spectrally-broad analysis windows at high frequencies and narrow windows at low frequencies. The WT is quite suitable for the analysis of nonstationary signals. A fast algorithm has been implemented in this work to compute the continuous wavelet transform of the SPCT flow signals using the Morl wavelet as a mother function. This algorithm is based on the fast Fourier transform (FFT) and it provides significant computational savings. The algorithm was incorporated in programs supported by the MATLAB package (version 5.1) running on a Pentium-I personal computer. The WT has been employed to estimate the time-scale energy distribution (scalogram) of actual SPCT flow signals made available for this work. They comprise six records generated by single particles flowing in an experimental system with each record having 256 data points sampled at a rate of 2000 sps. The scale value corresponding to the maximum density in the scalogram was used to estimate the characteristic frequency and then the flow velocity. The measured velocity was compared with those resulting from the periodogran method and the maximum entropy method, and were found to be in good agreement. The continuous wavelet transform has also been used to extract the instantaneous frequency of the flow signals and the discrete wavelet transform for denoising these signals. The WT gave a clear representation of the frequency and amplitude of the SPCT flow signals as they evolve in time. The waveforms obtained from the denoising experiments show that the proposed technique successfully removed most of the noise in the signal..