Brain-computer interface (BCI) systems enable the human brain to communicate with an external device bypassing the explicit pathways formed by natural nervous system. BCI decodes EEG signals recorded from the brain to extract important signatures related to subject’s intention. There are different EEG-based BCI systems such as P300 and steady-state visual evoked potential (SSVEP). To utilize the advantages of different types of BCls, several such BCls are carefully combined to form a hybrid BCI. This project will focus mainly on developing signal processing and classification technique for hybrid BCI speller. Hybrid BCI speller achieves significantly higher accuracy and information transfer rate (ITR) as features from two or more different signals are combined.