To determine eggshell quality, many external factors, such as appearance and cleanliness, can be detected by human graders. However, the inspection of the internal factor of strength of the eggshell has proven difficult. Some eggs develop hairline cracks due to low strength; this regularly happens in the process of egg production, causing economic loss and exposure to Salmonella contamination. These developed hairline cracks may be detectable. Although the effectiveness is in doubt, potential crack development of the eggshell can be predicted by observing the translucent mottling patterns of the eggshell. Early discarding of eggs with severe mottling has thus become a common practice in egg production to prevent further crack development and cross-contamination. However, accurately predicting potential crack development based on eggshell mottling is a challenge due to the irregularities of the mottling pattern itself, with difference in shape, size, distribution pattern, and color inhomogeneity. A pretrained convolutional neural network (CNN) model based on AlexNet architecture was proposed to extract learned image features of the mottling pattern on eggshells. The classification results by the fine-tuned CNN model was then compared to the classifications by the human graders as ground truthing. The fine-tuned CNN model performed on par to the human graders, with 91.8% similarity in classification rate. Visualization and analysis on intermediate activation strengths in the fine-tuned CNN model confirmed the consistency and traceability of the classification results. Our work underscores the potential of the CNN-based feature extraction and classification on eggs based on highly inhomogeneous mottling patterns on eggshell.