The use of conventional fluorescence microscopy for characterizing tissue pathological states is limited by overlapping spectra and the dependence on excitation power and fluorophore concentration. Fluorescence lifetime imaging microscopy (FLIM) can overcome these limitations due to its insensitivity to fluorophore concentration, excitation power and spectral similarity. This study investigates the diagnosis of early cervical cancer using FLIM and a neural network extreme learning machine classifier. A concurrently high sensitivity and specificity of 92.8% and 80.2%, respectively, were achieved. The results suggest that the proposed technique can be used to supplement the traditional histopathological examination of early cervical cancer.