In the last decade, a rapidly growing number of operando spectroscopy analyses have helped unravelling the electrochemical mechanism of lithium and post-lithium battery materials. The corresponding experiments usually produce large datasets containing many tens or hundreds of spectra. This considerable amount of data is calling for a suitable strategy for their treatment in a reliable way and within reasonable time frame. To this end, an alternative and innovating approach allowing one to extract all meaningful information from such data is the use of chemometric tools such as Principal Component Analysis (PCA) and multivariate curve resolution (MCR). PCA is generally used to discover the minimal particular structures in multivariate spectral data sets. In the case of operando spectroscopy data, it can be used to determine the number of independent components contributing to a complete series of collected spectra during electrochemical cycling. The number of principal components determined by PCA can then be used as the basis for MCR analysis, which allows the stepwise reconstruction of the “real” spectral components without needing any pre-existing model or any presumptive information about the system. In this paper, we will show how such approach can be effectively applied to different techniques, such as Mössbauer spectroscopy, X-ray absorption spectroscopy or transmission soft X-ray microscopy, for the comprehension of the electrochemical mechanisms in battery studies.