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Automatic sign language recognition is a recent field of research that has gained significance because it has the power to enhance communication between deaf people or hard of hearing and the rest of the population. In this paper, a convolutional neural network (CNN)-based model is described that can be used to identify American Sign Language (ASL) alphabets in realtime and in a computer vision system. The given model is trained and tested on the Sign Language MNIST dataset [1], where 24 fixed ASL letters (A -Y, without J or Z) are recognized. The test precision of the system is 96.49 percent, with 11 of 24 classes getting 100 percent accuracy and recall. The macro and weighted F1-score is 0.96 and 0.97 respectively, which shows that there is uniformity in performance across categories. The model is computationally minimal and can do real time inference and therefore is applicable to real life assistive applications.