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We provide an in-depth convolutional neural network (CNN) analysis of optical responses of liquid crystals (LCs) when exposed to different chemical environments. Our aim is to identify informative features that can be used to construct automated LC-based chemical sensors and shed some light on the underlying phenomenon that governs and distinguishes LC responses. Previous work demonstrated that, by using features extracted from AlexNet, grayscale micrographs of different LC responses can be classified with an accuracy of 99%. Reaching such high levels of accuracy, however, required the use of a large number of features (on the order of thousands), which was computationally intensive and clouded the physical interpretability of the dominant features. To address these issues, here we report a study on the effectiveness of using features extracted from color micrographs using VGG16, which is a more compact CNN than Alexnet. Our analysis reveals that features extracted from the first and second convolutional layers of VGG16 are sufficient to achieve a perfect classification accuracy while reducing the number of features to less than 100. The number of features is further reduced to 10 via recursive elimination with a minimal loss in classification accuracy (5–10%). This reduction procedure reveals that differences in spatial color patterns are developed within seconds in the LC response. From this, we conclude that hue distributions provide an informative set of features that can be used to characterize LC sensor responses. We also hypothesize that differences in the spatial correlation length of LC textures detected by VGG16 with DMMP and water likely reflect differences in the anchoring energy of the LC on the surface of the sensor. Our results hint at fresh approaches for the design of LC-based sensors based on the characterization of spontaneous fluctuations in the orientation (as opposed to changes in time-average orientations reported in the literature).