Recognizing handwritten alphanumeric characters is a complex issue computer vision and pattern recognition fields. However, recent years, usage of deep learning techniques has demonstrated encouraging outcomes in addressing this challenge. Here, we introduce deep learning-based approach which can be effectively used in identifying handwritten alphanumeric characters. Our proposed approach involves pre-processing the handwritten images to extract useful features along with training Convolutional Neural Network (CNN) using the extracted features. We utilize a blend of convolutional layers and fully connected layers to aid the model in learning the representations of the handwritten characters and classify them into their alphanumeric categories. Our approach evaluation is carried on a benchmark dataset of handwritten alphanumeric characters and achieves state-of-the-art results. Our work demonstrates effectiveness of various deep learning techniques for tackling the challenging problem of handwritten alphanumeric character recognition.