This paper is concerned with noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data arecategorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore multidimensional data, t-Distributed Stochastic Neighbor Embedding (t-SNE) and multidimensionalscaling (MDS) are applied to the RF data proximity matrix.When testing the developed tools on a set of data collectedfrom 109 subjects, 100% classification accuracy was obtainedon unseen data coming from two—healthy and pathological—classes. The accuracy of 80.7% was achieved when classifyingthe data into the healthy, cancerous, and noncancerous classes.The t-SNE and MDS mapping techniques facilitate data explorationaimed at identifying subjects belonging to a ”riskgroup”. It is expected that the developed tools will be of greathelp in preventive health care in laryngology.