Mass spectrometry (MS) is a powerful analytical technique for sensitive detection and accurate identification of molecules. Recent developments in this area lead to the emergence of a new technique, MS imaging (MSI), to map the spatial distributions of molecules in biological tissues. Large amounts of data are generated from MSI experiments, and they contain very rich chemical and spatial information of molecules in biological samples. However, the large size and complex structure of MSI datasets make it challenging to deeply understand the overall chemical information present in experimental data. Traditional MSI data analyses are generally carried out using manually selected ions to generate the MS images. Effective data analysis methods need to be developed to efficiently extract crucial information from large amounts of MSI data. We analyzed MSI data using multivariate curve resolution (MCR) and machine learning (ML) approaches. MCR was used to group molecules with similar patterns of spatial distribution. In ML analyses, both supervised (i.e., random forest) and unsupervised (i.e., DBSCAN and CLARA) were applied to process the MSI data. Compared with MCR approach, the unsupervised ML discovered sub-regions with subtle features within the major regions. Although unsupervised ML methods provided features generally similar to those obtained from MCR and supervised ML approaches, they are relatively more efficient for the analysis of large amount of MSI data.