System identification is an important area in process control and statistics. Identification of transfer function parameters provides significant benefits for research and in the design of closed-loop control systems. Despite over six decades of research and applications of system identification, only a handful of methods are currently available. Most current methods are somewhat complex and their results often uncertain or inconclusive when processing industrial data superimposed with noise and complex unmeasured disturbances. This paper illustrates the successful identification of transfer function parameters for multi-input/output systems amidst disturbance, noise with ultra-short duration data. This new method can easily be used in the control room environments, academic colleges and for research.