Signal processing techniques have lately been becoming more and more important in many agro-industrial applications.This signal processing-oriented approach is enabling new perspectives for many areas in the agro-industrial environment, such as real-time machinery monitoring, among others.The main goal of this thesis is to design, implement, and assess specific signal processing techniques enabling the monitoring of agro-industrial equipment in three senses: predictive maintenance, vehicle tracking, and measurement equipment.The proposed techniques contribute by expanding and extending, and even pioneering, current state-of-the-art techniques.The methodology followed along this thesis in order to reach the intended goals can be divided into five stages: review of the state of the art, hypothesis formulation, development and evaluation, result analysis, and results publication.The review of the state of the art was conducted in order to learn about other already existing techniques.After that, a research hypothesis was formulated to be used as the inception point of the research.Then, in the development and evaluation stage, an experimental setup was designed to develop and assess the proposed signal processing techniques.In the result analysis stage, the obtained results were compared against the literature.From this comparison, the validity of the research hypothesis could be checked.Finally, whenever redefinition of the research hypothesis was mandatory, the methodology went back to the hypothesis formulation stage.If the hypothesis was deemed to be valid, the obtained results were published.The aforementioned methodology was applied to three different agro-industrial problems: the predictive maintenance of an agricultural harvester, the kinematic tracking of a vehicle, and the monitoring of the flow rate through each individual nozzle in agricultural sprayers.A predictive maintenance approach, based on signal processing of the acquired mechanical vibration signals from an accelerometer placed on the chassis of an agricultural harvester, was proposed to estimate the mechanical status of several components of the machinery before it irretrievably cracks (first article of the compendium).A kinematic tracking approach, based on gathering several motion-related data and applying data fusion techniques, was proposed to enable a more accurate mechanical status estimation of a vehicle (second article of the compendium).An acoustic-based flow rate measurement approach, based on signal processing of the acquired acoustic signal gathered by a nearby microphone, was proposed to estimate, in real time, the actual flow rate coming out of each nozzle in agricultural sprayers (third article of the compendium).Three different experimental setups were used for each one of the three aforementioned agro-industrial problems tackled in this thesis.These setups were carefully designed so as to properly assess the developed methods.