The time adaptive SOM, or TASOM, is used to automatically adjust learning rate and neighborhood size of each neuron of the SOM network independently. Each neuron's learning rate is determined by a function of the distance between an input vector and its weight vector. The width of the neighborhood function is updated by a function of the distance between the weight vector of the neuron and the weight vectors of neighboring neurons. Only one time parameter initialization is sufficient throughout the lifetime of TASOM to work in stationary and nonstationary environments without retraining. In this paper, the TASOM is tested with standard data sets including the iris plant, breast cancer, and BUPA liver disease data for classification of input vectors. The tests carried out in stationary and nonstationary environments demonstrate that the TASOM can work for classification without the need for reinitializing the network parameters and weights.