AIM: To support probe-based confocal laser endomicroscopy (pCLE) diagnosis by designing a software for automated classification of colonic polyps. MATERIALS AND METHODS: Intravenous fluorescein pCLE imaging of colorectal lesions was performed on patients undergoing screening and surveillance colonoscopies, followed by polypectomies. All resected specimens were reviewed by a reference gastrointestinal pathologist blinded to pCLE information. Histopathology was used as criterion standard for the differentiation between neoplastic and non-neoplastic lesions. The pCLE video sequences, recorded for each polyp, were analyzed off-line by 2 expert endoscopists who were blinded to the endoscopic characteristics and histopathology. These pCLE videos, along with their histopathology diagnosis, were used to train the automated classification software which is a Content-Based Image Retrieval (CBIR) technique followed by k-nearest neighbor classification. The performances of the off-line diagnosis of pCLE videos established by the 2 expert endoscopists were compared with those of automated pCLE software classification. All evaluations were performed using leave-one-patient-out cross-validation to avoid bias. RESULTS: 135 colorectal lesions were imaged in 71 patients. Based on histopathology, 93 of these 135 lesions were neoplastic and 42 were non-neoplastic. The study finds no statistical significance for the difference between the performance of automated pCLE software classification (accuracy 89.6%, sensitivity 92.5%, specificity 83.3%, using leave-one-patient-out cross-validation) and the performance of the off-line diagnosis of pCLE videos established by the 2 expert endoscopists (accuracy 89.6%, sensitivity 91.4%, specificity 85.7%). There is very low power (< 6%) to detect the observed differences. The 95% confidence intervals for equivalence testing are: −0.073 to 0.073 for the accuracy, −0.068 to 0.089 for the sensitivity and −0.18 to 0.13 for the specificity. Besides, the classification software proposed in this study is not a "black box" but an informative tool based on the query by example model that produces, as intermediate results, visually similar annotated videos that are directly interpretable by the endoscopist. CONCLUSION: The proposed software for automated classification of pCLE videos of colonic polyps achieves high performance, comparable to that of off-line diagnosis of pCLE videos established by expert endoscopists.