Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single line to separate the spatial components of the cloud. In contrast, wide field spectral imaging with large spectral bandwidth in the (sub)mm domain now allows to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds. We aim at using multiple tracers (sensitive to different physical processes) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components). We use a machine learning clustering method (the Meanshift algorithm) to cluster pixels with similar molecular emission, ignoring spatial information. Simple radiative transfer models are used to interpret the astrophysical information uncovered by the clustering. A clustering analysis based only on the J=1-0 lines of 12CO, 13CO and C18O reveals distinct density/column density regimes (nH~100, 500, and >1000 cm-3), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the (1-0) lines of HCO+ and CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO+ and CN emission, which we relate to photochemical enrichment. We also find a tail of high CN/HCO+ intensity ratio in UV-illuminated regions. Finer distinctions in density classes (nH~7E3, and 4E4 cm-3) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO+ (1-0) lines. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift algorithm reveals how to decode the complex information available in molecular tracers.Read less <