A Brain-Computer interface (BCI) is a communication system that enables the generation of a control signal from brain signals such as sensorymotor rhythms and evoked potentials; therefore, it constitutes a novel communication option for people with severe motor disabilities (such as Amyotrophic Lateral Sclerosis patients). This paper presents the development of a P300-based BCI. This prototype uses a homemade six-channel electroencephalograph for the acquisition of the signals, and a visual stimulation matrix; since this matrix contains letters of the alphabet as well as images associated to them, it permits word-writing and the elaboration of messages with the images. To process the signals the software BCI2000 and MATLAB 7.0 were used. The latter was used to program three linear translation algorithms (Stepwise Linear Discriminant Analysis, Lineal Discriminant Analysis and Least Squares) to convert the brain signals into communication signals. These algorithms had a classification accuracy of 90.73 %, 95.75 % and 89.45 % respectively, when using raw data; and of 90.78%, 49.48 % and 53.9 %, when data was previously common-average filtered. The experimental setup was tested in ten healthy volunteers; 5 of them got a 100% success, 1 a 90% success, 2 an around 70% success and 2 a 50% success, in the online free-spelling tests.