The aim of this study is a transparent tool for analysis of voice (sustained phonation /a/) and query data capable of providing support in screening for laryngeal disorders. In this work, screening is concerned with identification of potentially pathological cases by classifying subject’s data into ’healthy’ and ’pathological’ classes as well as visual exploration of data and automatic decisions. A set of association rules and a decision tree, techniques lending themselves for exploration, were generated for pathology detection. Data pairwise similarities, estimated in a novel way, were mapped onto a 2D metric space for visual inspection and analysis. Accurate identification of pathological cases was observed on unseen subjects using the most discriminative query parameter and six audio parameters routinely used by otolaryngologists in a clinical practice: equal error rate (EER) of 11.1% was achieved using association rules and 10.2% using the decision tree. The EER was further reduced to 9.5% by combining results from these two classifiers. The developed solution can be a useful tool for Otolaryngology departments in diagnostics, education and exploratory tasks. © 2017 by the authors.