In deep geological drilling processes, the geological environment becomes more complex with the increasing of the drilling depth; consequently, the risks of downhole incidents get higher. If not discovered in time, these downhole incidents may develop to serious drilling accidents, causing significant financial and environmental losses. In this paper, a new method is proposed to diagnose downhole incidents by extracting trend features in multi-time scales and establishing a probabilistic neural network based diagnosis model. There are two major contributions: First, a feature extraction method is proposed to produce trend features from original process signals in different time scales; Second, an incident diagnosis method based on a broad probabilistic neural network is proposed to achieve better diagnosis performance in an expanded input space. Industrial case studies are presented to demonstrate the effectiveness and practicability of the proposed method. Results show that the proposed method has superior performance in diagnosing downhole incidents for geological drilling processes. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.