The identification of individual eukaryotic and prokaryotic cells is the backbone of clinical pathology and provides crucial information about the genesis and progression of a disease. While most commonly fluorescent-label based methods are applied, label-free methods, such as Raman spectroscopy, are elegant alternatives. A major disadvantage of Raman spectroscopy is the low signal yield resulting in long acquisition times, making it impractical for high-throughput clinical analysis. As a rule, Raman-based cell identification relies on high-resolution Raman spectra. This comes at a cost of detected Raman photons. In this letter we show that while the proper biochemical characterization of cells requires high-resolution Raman spectra, the proper classification of cells does not. By varying the slit-width between 50 µm and 500 µm it is possible to show that detected Raman signal from eukaryotic cells increased up to seven-fold. Raman-based cell classification was performed on three cancer cell lines: Jurkat, MiaPaca2, and Capan1, at three different resolutions 8 cm^-1 , 24 cm^-1 , and 48 cm^-1 . Moreover, we have simulated the resolution decrease due to low-diffraction gratings by binning neighboring pixels together. In both cases the cells were well classifiable using support vectors machine (SVM). For anyone working in the field of Raman spectroscopy this picture of Sir C.V. Raman is recognizable, even with reduced spatial resolution. Raman spectra of eukaryotic cells can also be recognized even with six fold reduced spectral resolution.