Laser-Induced Breakdown Spectroscopy (LIBS) has achieved promising performance as an elemental imaging technology, and considerable progress has been achieved in the development of LIBS over the last several years, which has led to great interest in the use of LIBS in various fields of applications. LIBS is a highly attractive technology that is distinguished by its table top instrumentation, speed of operation, and operation in ambient atmosphere, able to produce megapixel multi-elemental images with micrometric resolution (10 μm) and ppm-scale sensitivity. However, the points that limit the development of LIBS are undeniably the expertise and the time required to extract a relevant signal from the LIBS dataset. The complexity of the emission spectra (e.g., elemental responses, structure of the baseline), the high dynamic range of measurement (i.e., possibility to image major to trace elements), and the large number of spectra to process require new data analysis strategies. Such new strategies are particularly critical for multi-phase materials. In this paper, we report a new methodology based on the well-known Principal Component Analysis (PCA) approach for the multivariate hyperspectral analysis of LIBS images. The proposed methodology is designed for large, raw, and potentially complex series of LIBS spectra, that allows various and exhaustive levels of information to be extracted (including the characterization of mineral phases, assessment of the measurement and identification of isolated elements) and facilitates the manipulation of such hyperspectral datasets.