This paper presents a solution that automates Abaca fiber grading which would help the time-consuming baling of Abaca fiber produce. The study introduces an objective instrument paired with a system to automate the grade classification of Abaca fiber using Convolutional Neural Network (CNN). In this study, 140 sample images of abaca fibers were used, which were divided into two sets: 70 images; 10 per grade, each for training and testing. The input images were then scaled to 112x112 pixels. Next, using a customized version of VGGNet-16 CNN architecture, the training set images were used for training. Finally, the performance of the classifier was evaluated by computing the overall accuracy of the system and its Cohen kappa value. Based on the result, the classifier achieved 83% accuracy in correctly classifying the Abaca fiber grade of a sample image and obtained a Cohen kappa value of 0.52 -Weak, Level of Agreement. The implementation of this study would greatly help Abaca producers and traders ensure that their Abaca fiber would be graded fairly and efficiently to maximize their profit.