This work constitutes a first approach to automatically classify the urination medium for non-invasive sound based uroflowmetry tests. Often the voiding flow impacts the toilet wall (often made of ceramic) instead of the water. This causes a reduction in the amplitude of the recorded audio signal, and thus a reduction in the amplitude of the extracted envelope. Analysing the envelope alone, it is not possible to tell accurately if the reduction in the amplitude is due to a low voiding flow or an impact on the toilet walls. In this work, we carry out a study on the classification of sound uroflowmetry data depending on the medium where the urine impacts within the toilet: water or ceramic. In the analysis, a classification algorithm is proposed to identify the physical medium automatically based on the urination acoustics. The classification algorithm takes as input the frequency spectrum, the variance, and the kurtosis of the audio signal corresponding to a voiding event.Clinical relevance- Sound uroflowmetry has a strong correlation with the standard uroflowmetry. It is useful for the non-invasive detection of pathologies associated with the urinary tract as a support tool for information processing and screening. It consists of a characterization of the urinary flow patterns by capturing the sound generated when the urine stream impacts the water in the toilet. Identifying the medium which originates the sound is of paramount importance to better interpret the sound uroflowmetry.