The time adaptive self-organizing map (TASOM) network is a modified self-organizing map (SOM) network with adaptive learning rates and neighborhood sizes as its learning parameters. Every neuron in the TASOM has its own learning rate and neighborhood size. For each new input vector, the neighborhood size and learning rate of the winning neuron and the learning rates of its neighboring neurons are updated. A scaling vector is also employed in the TASOM algorithm for compensation against scaling transformations. Analysis of the updating rules of the algorithm reveals that the learning parameters may increase or decrease for adaptation to a changing environment, such that the minimum increase or decrease is achieved according to a specific measure. Several versions of the TASOM-based networks are proposed in this paper for different applications, including bilevel thresholding of grey level images, tracking of moving objects and their boundaries, and adaptive clustering. Simulation results show satisfactory performance of the proposed methods in the implemented applications.