Multispectral imaging can be used as a multimodal source to increase prediction accuracy of many machine learning algorithms by introducing additional spectral bands in data samples. This paper introduces a newly curated Multispectral Liquid 12-band (MeL12) dataset, consisting of 12 classes: eleven liquids and an empty container class. Multispectral images in this dataset have been captured using the PCO Ultraviolet, Grasshopper3 12.3 MP Color USB3 Vision, Mil-Rugged-High Resolution Snapshot Short Wave Infrared 1280JS, FLIR Medium Wave Infrared A6750sc and FLIR Long Wave Infrared T650sc cameras. Each of the classes initially results in a 640 × 480 × 12 data cube, where the 12 × 1 vector for each spectral pixel spans the spectral bands observed using the above-mentioned cameras and seven add-on bandpass optical filters. The usefulness of multispectral imaging in classification of liquids is demonstrated through the use of a support vector machine on MeL12 for classification of the 12 classes. The reported results are both encouraging and point to the need for additional work to improve liquid classification of harmless and dangerous liquids in high-risk environments, such as airports, concert halls, and political arenas, using multispectral imaging.