Background: Cytoplasmic viscosity (μc) is a key biomechanical parameter for evaluating the status of cellular cytoskeletons. Previous studies focused on white blood cells, but the data of cytoplasmic viscosity for tumor cells were missing. Methodology: Tumor cells (H1299, A549 and drug-treated H1299 with compromised cytoskeletons) were aspirated continuously through a micropipette at a pressure of -10 kPa or -5 kPa where aspiration lengths as a function of time were obtained and translated to cytoplasmic viscosity based on a theoretical Newtonian fluid model. Quartile coefficients of dispersion were quantified to evaluate the distributions of cytoplasmic viscosity within the same cell type while neural network based pattern recognitions were used to classify different cell types based on cytoplasmic viscosity. Results: The single-cell cytoplasmic viscosity with three quartiles and the quartile coefficient of dispersion were quantified as 16.7 Pa•S, 42.1 Pa•S, 110.3 Pa•S and 74% for H1299 cells at -10 kPa (ncell=652), 144.8 Pa•S, 489.8 Pa•S, 1390.7 Pa•S, and 81% for A549 cells at -10 kPa (ncell=785), 7.1 Pa•S, 13.7 Pa•S, 31.5 Pa•S, and 63% for CD-treated H1299 cells at -10 kPa (ncell=651) and 16.9 Pa•S, 48.2 Pa•S, 150.2 Pa•S, and 80% for H1299 cells at -5 kPa (ncell=600), respectively. Neural network based pattern recognition produced successful classification rates of 76.7% for H1299 vs. A549, 67.0% for H1299 vs. drug-treated H1299 and 50.3% for H1299 at -5 kPa and -10 kPa. Conclusion: Variations of cytoplasmic viscosity were observed within the same cell type and among different cell types, suggesting the potential role of cytoplasmic viscosity in cell status evaluation and cell type classification.