Mass spectrometry facilitates cutting edge advancements in many fields. Although instrumentation has advanced dramatically in the last 100 years, data processing algorithms have not kept pace. Without sensitive and accurate signal segmentation algorithms, the utility of mass spectrometry is limited. In this dissertation, we provide an overview and analysis of mass spectrometry data processing. A tutorial to ease the learning curve for those outside the field is provided. We draw attention to the lack of critical evaluation in the field and describe the resulting effects, including a glut of algorithm contributions of questionable novel contribution. To facilitate increased critical evaluation, we show the importance of a modular paradigm for mass spectrometry data processing through highlighting the impact of data processing algorithm choice upon experimental results. Our novel controlled vocabulary is presented with the aim of facilitating literature reviews for comparisons. We propose a novel nomenclature and mathematical characterization of mass spectrometry data. We present several novel algorithms for mass spectrometry data segmentation that outperform existing standard approaches. We end with an overview of future research which will continue to advance the state of the art in mass spectrometry data processing.