The problem considered here involves the design and application of a recursive algorithm to extract and predict the position of an object in a 3D environment from one feature correspondence from a monocular image sequence. Translational model involves an object moving in a parabolic path using projectile physics. A state-space model is constructed incorporating kinematic states, and recursive techniques are used to estimate the state vector as a function of time. The measured data are the noisy image plane coordinates of object match taken from image in the sequence. Image plane noise levels are allowed and investigated. The problem is formulated as a tracking problem, which can use an arbitrary large number of images in a sequence. The recursive estimation is done using Recursive Least Squares (RLS). Results on both synthetic and real imagery illustrate the performance of the estimator.