A new correction method is developed for the effect of the length-to-diameter (L/D) ratio on critical heat flux (CHF) by applying artificial neural networks and conventional regression techniques to the KAIST CHF data base for water flow in uniformly-heated, vertical round tubes. It consists of two parts: (a) a threshold L/D over which the length effect becomes negligible; and (b) a L/D correction factor for channels with L/D less than the threshold L/D. The proposed correction method is validated with the experimental data in the original database and a new data set obtained from the KAIST intermediate pressure loop. The proposed method will be useful in the following applications: (a) to predict the CHF for short tubes using CHF models which are based on the data for sufficiently long channels; (b) to define the experimental data which can be used for development of local-condition type CHF correlations; and (c) to convert the CHF data from short channels into CHF data for standard long channels for utilization in correlation development. (C) 2000 Published by Elsevier Science S.A. All rights reserved.