Classification of pathological vs. normal infant cries is used to infer the infant’s health conditions. Such an approach can be beneficial in many situations and even to save infants’ lives. In this paper, we propose a novel classification system based on the Modified Group Delay Cepstral Coefficients (MGDCC), for classifying infant cries. We investigate generalizability of proposed MGDCC features. The Convolutional Neural Network (CNN) was used as a pattern classifier in this study. Proposed MGDCC features are found to perform better than widely used spectral features, such as Mel Frequency Cepstral Coefficients (MFCC), Linear Frequency Cepstral Coefficients (LFCC), and Group Delay Cepstral Coefficients (GDCC). Experiments are performed on two datasets namely, Baby Chillanto (D1) dataset, and DA-IICT Infant Cry (D2) corpus and for various experimental evaluation factors, such as noise robustness under signal degradation conditions, cross-database scenario, and analysis of latency period. We obtained 2.25% increase accuracy as compared to existing optimal accuracy for proposed task. Better performance of MGDCC is may be due to its capability to implicitly capture time dependencies in the sequence of audio samples via fourier transform phase information.