Relating pressure fluctuations to the various flow regimes in dilute phase pneumatic conveying systems and exploring the effects of the variations of a number of intrinsic flow parameters through the analysis of complex pressure signals are of importance. To explore the pressure fluctuations, a series of experiments were conducted by monitoring the pressure drop and its fluctuations in horizontal and vertical pipe sections of an experimental conveying rig of industrial relevance. Observed pressure signals contained not only the effects of flow rates of conveyed polymeric solid particles and the conveying air, but also the effects of the pipe orientation, solids feeder, and the air blower employed. Time series of the observed pressure signals were analyzed in order to obtain information about the relations based on four different analysis techniques: power spectral density (PSD) analysis, phase space diagram, rescaled range analysis, and wavelet analysis. The sampling frequency of the study was determined through the application of the PSD analysis. Variations in gas and particle flow rates were found that could be traced by the locations of the dominant peaks on the frequency axis of spectrum plots generated from the PSD analysis. Additionally, the level of multiplicity of such dominant PSD peaks pointed to the differences in the flow behavior between the interconnected vertical and horizontal pipe sections. Variations in the Hurst's exponent of the rescaled range analysis technique with the solids loading ratios were found relatable to the observed flow regimes prevailing in the pneumatic conveying system studied. Eccentricity ratios and the number of attractors of the phase space diagrams provided further information about the effects of the changes in solids loading ratios on the flow behavior and the flow mode, respectively. The wavelet analysis, through its detailed signals at different scale levels, helped to give information from the pressure fluctuations. Unique frequencies were determined that corresponded to certain flow behaviors and devices, gas particle interactions, the mechanical blower, and feeder units. This finding enabled the investigation of the intrinsic flow behavior by filtering out the signals caused by such mechanical devices.