Image classification is typically performed with highly trained feed-forward machine learning algorithms like deep neural networks and support vector machines. The image can be treated as a time-series input when applied to the network multiple times, opening the way for recurrent neural networks to perform tasks like image classification, semantic segmentation, and auto-encoding. In this report, the MNIST handwritten digit dataset is used as a benchmark to evaluate metrics of a modified Echo State Network for static image classification. The image array is passed through a noise filter multiple times as the Echo State Network converges to a classification. This highly dynamic approach easily adapts to sequential image (video) tasks like object tracking and is effective with small datasets. Classification rates reach 95.3% with sample size of 10000 handwritten digits and training time of approximately 5 minutes. Progression of this research enables discrete image and time-series classification under a single algorithm, with low computing power and memory requirements.