Distinguishing different textile fibers is important for recycling waste textiles. Most studies on non-destructive optical textile identification have focused on classifying different synthetic and natural fibers but chemical recycling requires more detailed information on fiber composition and polymer properties. Here, we report the use of near infrared imaging spectroscopy and chemometrics for classifying natural and regenerated cellulose fibers. Our classifiers trained on images of consumer textiles showed 100% true positive rates based on model cross-validation and correctly identified on average 8-9 out of 10 test set pixels using images of specifically made cotton, viscose and lyocell samples of known compositions. These results are significant as they indicate the possibility to monitor and control fiber dosing and subsequent dope viscosity during chemical recycling of cellulose fibers. Our results also suggested the possibility to identify fibers purely based on polymer chain length. This finding opens the possibility to indirectly estimate dope viscosity and creates entirely new hypotheses for combining imaging spectroscopy with classification and regression methods within the broader field of cellulose modification.