  We consider two models of Hopfield-like associative memory with $q$-valued
neurons: Potts-glass neural network (PGNN) and parametrical neural network
(PNN). In these models neurons can be in more than two different states. The
models have the record characteristics of its storage capacity and noise
immunity, and significantly exceed the Hopfield model. We present a uniform
formalism allowing us to describe both PNN and PGNN. This networks inherent
mechanisms, responsible for outstanding recognizing properties, are clarified.
