In this paper, a neural competitive learning tree is introduced as a computationally attractive scheme for adaptive density estimation and novelty detection. The learning rule yields equiprobable quantization of the input space and provides an adaptive focusing mechanism capable of tracking time-varying distributions. It is shown by simulation that the neural tree performs reasonably well while being much faster than any of the other competitive learning algorithms.