Mass spectrometry experiments collect large amounts of data that must be processed and interpreted in order to generate useful results, such as concentrations of specified analytes. The software that performs these processing tasks for LC-MS/MS data must handle a diverse set of tasks, including identifying and integrating peaks, calculating concentrations using appropriate calibration schemes, and assessing the quality of the resulting measurements. In this chapter, we present common methodologies that are utilized in proprietary and open-source software packages for mass spectrometric data analysis. Peak identification techniques such as derivative- and wavelet-based approaches are presented, along with common smoothing techniques such as Savitzky–Golay filters. After numeric integration of the resulting peak and possible normalization using stable isotope-labeled internal standards, external calibration can be used to provide a reliable concentration assessment. We discuss common calibration weighting functions, along with the form of the curve that is fit. After assessing the goodness of this calibration fit, final analyte concentrations can be derived. Overall control of this process then proceeds using standard laboratory techniques, such as Levey-Jennings plots in conjunction with Westgard rules. Additional considerations for applying these techniques in the context of highly multiplexed assays are briefly discussed. Finally, for applications that require direct access to unprocessed mass spectrometry data, we discuss common data interchange formats that provide access to raw traces and other similar data types.