The discrimination of cloudy from cloud-free pixels is required in almost any estimate of a parameter retrieved from satellite data in the ultraviolet (UV), visible (VIS) or infrared (IR) parts of the electromagnetic spectrum. In this paper we report on the development of a neural network (NN) algorithm to estimate cloud fractions using radiances measured at the top of the atmosphere with the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained from the application of two different types of neural networks, i.e., extreme learning machine (ELM) and back propagation (BP). The NNs were trained with an OMI data sets existing of six orbits, tested with three other orbits and validated with another two orbits. The results were evaluated by comparison with cloud fractions available from the MODerate Resolution Imaging Spectrometer (MODIS) flying on Aqua in the same constellation as OMI, i.e., with minimal time difference between the OMI and MODIS observations. The results from the ELM and BP NNs are compared. They both deliver cloud fraction estimates in a fast and automated way, and they both performs generally well in the validation. However, over highly reflective surfaces, such as desert, or in the presence of dust layers in the atmosphere, the cloud fractions are not well predicted by the neural network. Over ocean the two NNs work equally well, but over land ELM performs better.