Abstract. We developed a simple algorithm to classify clouds based on global radiation and cloud base height measured by pyranometer and ceilometer, respectively. We separated clouds into seven different classes (stratus, stratocumulus, cumulus, nimbostratus, altocumulus + altostratus, cirrus + cirrocumulus + cirrostratus and clear sky + cirrus). We also included classes for cumulus and cirrus clouds causing global radiation enhancement, and we classified multilayered clouds, when captured by the ceilometer, based on their height and characteristics (transmittance, patchiness and uniformity). The overall performance of the algorithm was nearly 70 % when compared with classification by an observer using total-sky images. The performance was best for clouds having well-distinguishable effects on solar radiation: nimbostratus clouds were classified correctly in 100 % of the cases. The worst performance corresponds to cirriform clouds (50 %). Although the overall performance of the algorithm was good, it is likely to miss the occurrences of high and multilayered clouds. This is due to the technical limits of the instrumentation: the vertical detection range of the ceilometer and occultation of the laser pulse by the lowest cloud layer. We examined the use of clearness index, which is defined as a ratio between measured global radiation and modeled radiation at the top of the atmosphere, as an indicator of clear-sky conditions. Our results show that cumulus, altocumulus, altostratus and cirriform clouds can be present when the index indicates clear-sky conditions. Those conditions have previously been associated with enhanced aerosol formation under clear skies. This is an important finding especially in the case of low clouds coupled to the surface, which can influence aerosol population via aerosol–cloud interactions. Overall, caution is required when the clearness index is used in the analysis of processes affected by partitioning of radiation by clouds.