In this work, a scalable hardware/software architecture for ELM is presented, and the details of its implementation on a field programmable gate array (FPGA) are analyzed. The proposed solution provides high speed, small size, low power consumption, autonomy, and true capability for real-time adaptation (i.e. the learning stage is performed on-chip). The developed system is able to deal with highly demanding multiclass classification problems. Two real-world applications are presented, a benchmark problem of the Landsat images database, and a novel driver identification system for smart car applications. Experimental results that validate the proposal are provided.