This paper presents a new procedure called TREEFAM for estimating ultrametric tree structures from proximity data confounded by differential stimulus familiarity. The objective is to quantitatively filter out effects of stimulus unfamiliarity. Superiority of TREEFAM over conventional methods is illustrated through a Monte Carlo study and an empirical example. (SLD)