A prototype programmable analog neural computer has been assembled from over 100 custom VLSI modules containing neurons, synapses, routing switches, and programmable synaptic time constants. The modules are directly interconnected and arbitrary network configurations can be programmed. Connection symmetry and modular construction allow expansion of the network to any size. The network runs in real time analog mode, but connection architecture as well as neuron and synapse parameters are controlled by a digital host. Network performance is monitored by the host through an A/D interface and used in the implementation of learning algorithms. The machine is intended for real time, real world computations. In its current configuration maximal speed is equivalent to that of a digital machine capable of 1011 FLOPS. The programmable synaptic time constants permit the real time computation of temporal patterns as they occur in speech and other acoustic signals. Several applications involving the dynamic decomposition and recognition of acoustical patterns including speech signals (phonemes) are described. The decomposition network is loosely based on the primary auditory system of higher vertebrates. It extracts and represents by the activity in different neuron arrays the following pattern primitives: frequency, bandwidth, amplitude, amplitude modulation, amplitude modulation frequency, frequency modulation, frequency modulation frequency, duration, sequence. The frequency tuned units are the first stage and form the input space for subsequent stages that extract the other primitives, e.g., bandwidth, amplitude modulation, etc., for different frequency bands. Acoustic input generates highly specific, relatively sparse distributed activity in this feature space, which is decoded and recognized by units trained by specific input patterns such as phonemes or diphones or active sonar patterns. Through simple feedback connections in conjunction with synaptic time constants the neurons can be transformed into spiking units resembling biological neurons and networks of such units can be used in simulations of small biological neural assemblies. A larger machine, with much higher component count, speed and density as well as higher resolution of synaptic weights and time constants is currently under development. Some specific design issues for the construction of larger machines including selection of optimal component parameters, high density interconnect methods, and control software are discussed.