The Cleft and Lip Palate (CLP) is a malformation with high recurrence in Colombia, which affects the ability of the phonation system, making difficult the effective communication of the patient. This research seeks to find patterns that enable to detect hypernasality without using invasive diagnostic methods. We performed an analysis of a large range of acoustic features to identify those capable of discriminating hypernasality. The analyzed features include: Teager energy operator (TEO), linear predictive coding (LPC), Mel Frequency Cepstral Coefficients (MFCC), Pitch, Jitter, Shimmer, and the first three formants together with the bandwidth of the first formant. With the correct configuration is achieved discriminant patterns classify 99 percent of patients hypernasal of the database with a false positive rate of less than 1 percent of healthy patients, which are promising results as a starting point for creating a tool for automatic noninvasive detection of hypernasality.