This paper sets forth and illustrates some techniques for parameter identification (PId) of a nonlinear state model that approximates the dynamical behavior of the humoral immune response of a human to Haemophilus influenzae Type-b. The natural physiological time-separation of the primary, late follicular, and secondary immune responses of this biological process allows us to divide the PId problem into a sequence of smaller PId sub-problems. To reduce the dimension of the PId even further, coupling effects are minimized or eliminated by temporarily replacing variables and/or certain other functions of variables by approximate a priori known time functions called exogenous inputs. This sequence of low dimensional PId problems entails matching a set of one or two parameters at each step to a time-attribute pair defined as a maximum or minimum measured concentration level in a given time window. The identification sub-problem solution reduces to the inverse of an approximate local parameter-to-measurement map. The techniques presented herein are applicable to other nonlinear systems which exhibit similar time-sequenced properties.