Classification is at the very center of the supervised learning. In this work, we propose a novel algorithm to classify the test data set with the aid of a vector field, emanating from the training data set. In particular, the vector field is constructed such that the location of each training data point becomes a local minimum of the potential. The test data points are allowed to evolve under the influence of the velocity field, generated by the training data set, and thereby would be converging to the domain of attractions of different classes. The proposed approach avoids explicit computation of the separating hyper-plane like Support Vector Machine, which becomes difficult, if the structure of the separating hyper-plane is nonlinear. The proposed method is specially suited for online learning problems, as the model training does not involve any additional time. Comparative simulation studies are presented over data sets coming from three practical Machine Learning benchmark problems.