The uniaxial compressive strength of rock material (UCS) is one of the fundamental input parameters for engineering applications to be constructed on/in rock masses such as deep slopes, tunnels and dams. However, preparation of the high quality cores for laboratory studies is generally difficult for some types of rock such as laminated and/or fragmented rock material. To overcome this difficulty empirical prediction models were developed by considering some input parameters. Geological mixtures composed of rock blocks surrounded by weak matrix material are known as Block-In-Matrix-Rock (Bimrock) in literature. Agglomerate is a special type of Bimrock, which is composed of andesite fragments surrounded by tuff matrix and it is an example of Volcanic Bimrock. Preparation of core samples for experimental studies from agglomerate is problematic due to the strength contrast between andesite rock fragments and tuff matrix. To overcome these difficulties, some prediction tools have been studied by regression analyses in the literature. In this study, Artificial Neural Network (ANN) as a prediction tool was used to construct a model for prediction of overall UCS of Volcanic Bimrock. While Volumetric Block Proportion (VBP), Volumetric Block Count (VBC) and fractal dimensions (1 and 2 dimensional) were selected as input parameters, normalized overall uniaxal strength of agglomerate to uniaxal compressive strength of tuff matrix is output parameter. Fractal geometry has been used as popular method to define irregular shapes as a quantity in literature. The boundary strength between an-desite fragments and tuff matrix is also sensitive to fragment shape and surface roughness of andesite fragments. Therefore fractal dimensions were selected as input parameters to incorporate this effect on boundary strength. While previously developed computer code FRACRUN was used to determine average fractal dimension of andesite fragments in agglomerate cores, previously developed computer code ANNES was used for ANN based model construction. In addition, similar to Volumetric Joint Count (Jv) which is widely used in rock mass characterization, Volumetric Block Count (VBC) was defined as another input parameter for determination of Bimrock UCS considering some of studies about performed in literature. The highest prediction performance was obtained from the model which considers Volumetric Block Proportion (VBP), Volumetric Block Count (VBC) and 1D fractal dimension as inputs.