Objective: The objective of this study is to evaluate the efficacy of laser-induced fluorescence (LIF) data obtained at 325-nm pulsed laser excitation for the discrimination of normal, benign, and malignant ovarian tissues. Background Data: Several studies have reported that the autofluorescence technique has a high specificity and sensitivity for discrimination between diseased and non-diseased tissues of various cancers, and also has the advantages of being non-invasive and producing a real-time diagnosis. When using this technique on ovarian tissues in most of the previously reported studies, multivariate statistical tools were used and classification analyses were carried out. Materials and Methods: Autofluorescence spectra of normal, benign, and malignant ovarian tissues were recorded with 325-nm pulsed laser excitation in the spectral region from 350–600 nm in vitro. The spectral analysis for discrimination between the different types of tissues was carried out using principal component analysis (PCA)-based non-parametric k-nearest neighbor (k-NN) analysis. Results: A total of 97 (34 normal, 33 benign, and 30 malignant) spectra were obtained from 22 subjects with normal, benign, and malignant tissues. The discrimination analysis of data using a PCA-based k-NN algorithm showed very good discrimination. The performance of the analysis was evaluated by calculating statistical parameters, specificity, sensitivity, and accuracy and were found to be 100%, 90.90%, and 94.2%, respectively. Conclusion: The results show that the discrimination of normal, benign, and malignant ovarian conditions can be achieved quite successfully using LIF.